From 40c1aaa9092ccfb23641d3b897d70d5085f5851a Mon Sep 17 00:00:00 2001 From: "Chendi.Xue" Date: Thu, 13 Jun 2024 20:33:28 -0500 Subject: [PATCH] Add Ray version for multi file process (#119) * add ray version document to redis Signed-off-by: Chendi Xue * update test Signed-off-by: Chendi Xue * Add test Signed-off-by: Chendi Xue * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add TIMEOUT in container environment and return status Signed-off-by: Chendi Xue * rebase on new folder layout Signed-off-by: Chendi Xue * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Signed-off-by: Chendi Xue Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- comps/dataprep/redis/README.md | 69 +++- comps/dataprep/redis/langchain/config.py | 1 + comps/dataprep/redis/langchain_ray/config.py | 68 ++++ .../redis/langchain_ray/docker/Dockerfile | 38 +++ .../langchain_ray/prepare_doc_redis_on_ray.py | 322 ++++++++++++++++++ .../redis/langchain_ray/requirements.txt | 22 ++ .../redis/langchain_ray/schema_dim_768.yml | 14 + comps/dataprep/redis/test_data.pdf | Bin 0 -> 1083893 bytes comps/dataprep/utils.py | 54 ++- tests/test_ingestion.sh | 84 +++++ 10 files changed, 667 insertions(+), 5 deletions(-) create mode 100644 comps/dataprep/redis/langchain_ray/config.py create mode 100644 comps/dataprep/redis/langchain_ray/docker/Dockerfile create mode 100644 comps/dataprep/redis/langchain_ray/prepare_doc_redis_on_ray.py create mode 100644 comps/dataprep/redis/langchain_ray/requirements.txt create mode 100644 comps/dataprep/redis/langchain_ray/schema_dim_768.yml create mode 100644 comps/dataprep/redis/test_data.pdf create mode 100644 tests/test_ingestion.sh diff --git a/comps/dataprep/redis/README.md b/comps/dataprep/redis/README.md index 6aa080141..10b7d762a 100644 --- a/comps/dataprep/redis/README.md +++ b/comps/dataprep/redis/README.md @@ -1,6 +1,6 @@ # Dataprep Microservice with Redis -For dataprep microservice, we provide two frameworks: `Langchain` and `LlamaIndex`. +For dataprep microservice, we provide two frameworks: `Langchain` and `LlamaIndex`. We also provide `Langchain_ray` which uses ray to parallel the data prep for multi-file performance improvement(observed 5x - 15x speedup by processing 1000 files/links.). We organized these two folders in the same way, so you can use either framework for dataprep microservice with the following constructions. @@ -8,6 +8,8 @@ We organized these two folders in the same way, so you can use either framework ## 1.1 Install Requirements +- option 1: Install Single-process version (for 1-10 files processing) + ```bash # for langchain cd langchain @@ -16,6 +18,12 @@ cd llama_index pip install -r requirements.txt ``` +- option 2: Install multi-process version (for >10 files processing) + +```bash +cd langchain_ray; pip install -r requirements_ray.txt +``` + ## 1.2 Start Redis Stack Server Please refer to this [readme](../../../vectorstores/langchain/redis/README.md). @@ -34,10 +42,18 @@ export LANGCHAIN_PROJECT="opea/gen-ai-comps:dataprep" Start document preparation microservice for Redis with below command. +- option 1: Start single-process version (for 1-10 files processing) + ```bash python prepare_doc_redis.py ``` +- option 2: Start multi-process version (for >10 files processing) + +```bash +python prepare_doc_redis_on_ray.py +``` + # 🚀2. Start Microservice with Docker (Option 2) ## 2.1 Start Redis Stack Server @@ -58,6 +74,8 @@ export LANGCHAIN_PROJECT="opea/dataprep" - Build docker image with langchain +* option 1: Start single-process version (for 1-10 files processing) + ```bash cd ../../../../ docker build -t opea/dataprep-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain/docker/Dockerfile . @@ -70,13 +88,28 @@ cd ../../../../ docker build -t opea/dataprep-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/llama_index/docker/Dockerfile . ``` +- option 2: Start multi-process version (for >10 files processing) + +```bash +cd ../../../../ +docker build -t opea/dataprep-on-ray-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain_ray/docker/Dockerfile . +``` + ## 2.4 Run Docker with CLI (Option A) +- option 1: Start single-process version (for 1-10 files processing) + +```bash +docker run -d --name="dataprep-redis-server" -p 6007:6007 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT opea/dataprep-redis:latest +``` + +- option 2: Start multi-process version (for >10 files processing) + ```bash -docker run -d --name="dataprep-redis-server" -p 6007:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT opea/dataprep-redis:latest +docker run -d --name="dataprep-redis-server" -p 6007:6007 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT -e TIMEOUT_SECONDS=600 opea/dataprep-on-ray-redis:latest ``` -## 2.5 Run with Docker Compose (Option B) +## 2.5 Run with Docker Compose (Option B - deprecated, will move to genAIExample in future) ```bash # for langchain @@ -86,7 +119,13 @@ cd comps/dataprep/redis/llama_index/docker docker compose -f docker-compose-dataprep-redis.yaml up -d ``` -# 🚀3. Consume Microservice +# 🚀3. Status Microservice + +```bash +docker container logs -f dataprep-redis-server +``` + +# 🚀4. Consume Microservice Once document preparation microservice for Redis is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database. @@ -96,3 +135,25 @@ curl -X POST \ -d '{"path":"/path/to/document"}' \ http://localhost:6007/v1/dataprep ``` + +or + +```python +import requests +import json + +proxies = {"http": ""} +url = "http://localhost:6007/v1/dataprep" +urls = [ + "https://towardsdatascience.com/no-gpu-no-party-fine-tune-bert-for-sentiment-analysis-with-vertex-ai-custom-jobs-d8fc410e908b?source=rss----7f60cf5620c9---4" +] +payload = {"link_list": json.dumps(urls)} + +try: + resp = requests.post(url=url, data=payload, proxies=proxies) + print(resp.text) + resp.raise_for_status() # Raise an exception for unsuccessful HTTP status codes + print("Request successful!") +except requests.exceptions.RequestException as e: + print("An error occurred:", e) +``` diff --git a/comps/dataprep/redis/langchain/config.py b/comps/dataprep/redis/langchain/config.py index e7dd205d1..05e155bfc 100644 --- a/comps/dataprep/redis/langchain/config.py +++ b/comps/dataprep/redis/langchain/config.py @@ -63,5 +63,6 @@ def format_redis_conn_from_env(): current_file_path = os.path.abspath(__file__) parent_dir = os.path.dirname(current_file_path) REDIS_SCHEMA = os.getenv("REDIS_SCHEMA", "schema_dim_768.yml") +TIMEOUT_SECONDS = int(os.getenv("TIMEOUT_SECONDS", 600)) schema_path = os.path.join(parent_dir, REDIS_SCHEMA) INDEX_SCHEMA = schema_path diff --git a/comps/dataprep/redis/langchain_ray/config.py b/comps/dataprep/redis/langchain_ray/config.py new file mode 100644 index 000000000..05e155bfc --- /dev/null +++ b/comps/dataprep/redis/langchain_ray/config.py @@ -0,0 +1,68 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +import os + +# Embedding model + +EMBED_MODEL = os.getenv("EMBED_MODEL", "BAAI/bge-base-en-v1.5") + +# Redis Connection Information +REDIS_HOST = os.getenv("REDIS_HOST", "localhost") +REDIS_PORT = int(os.getenv("REDIS_PORT", 6379)) + + +def get_boolean_env_var(var_name, default_value=False): + """Retrieve the boolean value of an environment variable. + + Args: + var_name (str): The name of the environment variable to retrieve. + default_value (bool): The default value to return if the variable + is not found. + + Returns: + bool: The value of the environment variable, interpreted as a boolean. + """ + true_values = {"true", "1", "t", "y", "yes"} + false_values = {"false", "0", "f", "n", "no"} + + # Retrieve the environment variable's value + value = os.getenv(var_name, "").lower() + + # Decide the boolean value based on the content of the string + if value in true_values: + return True + elif value in false_values: + return False + else: + return default_value + + +def format_redis_conn_from_env(): + redis_url = os.getenv("REDIS_URL", None) + if redis_url: + return redis_url + else: + using_ssl = get_boolean_env_var("REDIS_SSL", False) + start = "rediss://" if using_ssl else "redis://" + + # if using RBAC + password = os.getenv("REDIS_PASSWORD", None) + username = os.getenv("REDIS_USERNAME", "default") + if password is not None: + start += f"{username}:{password}@" + + return start + f"{REDIS_HOST}:{REDIS_PORT}" + + +REDIS_URL = format_redis_conn_from_env() + +# Vector Index Configuration +INDEX_NAME = os.getenv("INDEX_NAME", "rag-redis") + +current_file_path = os.path.abspath(__file__) +parent_dir = os.path.dirname(current_file_path) +REDIS_SCHEMA = os.getenv("REDIS_SCHEMA", "schema_dim_768.yml") +TIMEOUT_SECONDS = int(os.getenv("TIMEOUT_SECONDS", 600)) +schema_path = os.path.join(parent_dir, REDIS_SCHEMA) +INDEX_SCHEMA = schema_path diff --git a/comps/dataprep/redis/langchain_ray/docker/Dockerfile b/comps/dataprep/redis/langchain_ray/docker/Dockerfile new file mode 100644 index 000000000..aec2506ec --- /dev/null +++ b/comps/dataprep/redis/langchain_ray/docker/Dockerfile @@ -0,0 +1,38 @@ + +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +FROM python:3.11-slim + +ENV LANG C.UTF-8 + +RUN apt-get update -y && apt-get install -y --no-install-recommends --fix-missing \ + build-essential \ + libgl1-mesa-glx \ + libjemalloc-dev \ + vim + +RUN useradd -m -s /bin/bash user && \ + mkdir -p /home/user && \ + chown -R user /home/user/ + +USER user + +COPY comps /home/user/comps + +RUN pip install --no-cache-dir --upgrade pip setuptools && \ + pip install --no-cache-dir -r /home/user/comps/dataprep/redis/langchain_ray/requirements.txt + +ENV PYTHONPATH=$PYTHONPATH:/home/user + +USER root + +RUN mkdir -p /home/user/comps/dataprep/redis/langchain_ray/uploaded_files && chown -R user /home/user/comps/dataprep/redis/langchain_ray/uploaded_files +RUN mkdir -p /home/user/comps/dataprep/redis/langchain_ray/status && chown -R user /home/user/comps/dataprep/redis/langchain_ray/status + +USER user + +WORKDIR /home/user/comps/dataprep/redis/langchain_ray + +ENTRYPOINT ["python", "prepare_doc_redis_on_ray.py"] + diff --git a/comps/dataprep/redis/langchain_ray/prepare_doc_redis_on_ray.py b/comps/dataprep/redis/langchain_ray/prepare_doc_redis_on_ray.py new file mode 100644 index 000000000..be8240f03 --- /dev/null +++ b/comps/dataprep/redis/langchain_ray/prepare_doc_redis_on_ray.py @@ -0,0 +1,322 @@ +# Copyright (c) 2024 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import os +import pathlib +import sys +from pathlib import Path +from typing import Callable, List, Optional, Union + +import pandas as pd +from config import EMBED_MODEL, INDEX_NAME, INDEX_SCHEMA, REDIS_URL, TIMEOUT_SECONDS +from fastapi import File, Form, HTTPException, UploadFile +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_community.embeddings import HuggingFaceBgeEmbeddings, HuggingFaceHubEmbeddings +from langchain_community.vectorstores import Redis +from langsmith import traceable + +cur_path = pathlib.Path(__file__).parent.resolve() +comps_path = os.path.join(cur_path, "../../../../") +sys.path.append(comps_path) +import hashlib +import timeit +from typing import TYPE_CHECKING, Any, Dict, Iterator + +import pyarrow +import ray +from ray.data.block import Block +from ray.data.datasource import FileBasedDatasource +from tqdm import tqdm + +from comps import DocPath, opea_microservices, register_microservice +from comps.dataprep.utils import Timer, document_loader, parse_html, timeout + +tei_embedding_endpoint = os.getenv("TEI_ENDPOINT") +debug = False + + +def prepare_env(enable_ray=False, pip_requirements=None): + if enable_ray: + import ray + + if ray.is_initialized(): + ray.shutdown() + if pip_requirements is not None: + ray.init(runtime_env={"pip": pip_requirements, "env_vars": {"PYTHONPATH": comps_path}}) + else: + ray.init(runtime_env={"env_vars": {"PYTHONPATH": comps_path}}) + + +def generate_log_name(file_list): + file_set = f"{sorted(file_list)}" + # print(f"file_set: {file_set}") + md5_str = hashlib.md5(file_set.encode()).hexdigest() + return f"status/status_{md5_str}.log" + + +def get_failable_with_time(callable): + def failable_callable(*args, **kwargs): + start_time = timeit.default_timer() + try: + content = callable(*args, **kwargs) + error = None + except Exception as e: + content = None + error = str(e) + end_time = timeit.default_timer() + return content, error, f"{'%.3f' % (end_time - start_time)}" + + return failable_callable + + +def get_max_cpus(total_num_tasks): + num_cpus_available = os.cpu_count() + num_cpus_per_task = num_cpus_available // total_num_tasks + if num_cpus_per_task == 0: + return 8 + return num_cpus_per_task + + +def save_logs(log_name, data): + df = pd.DataFrame.from_records(data) + try: + dir_path = os.path.dirname(log_name) + if not os.path.exists(dir_path): + os.makedirs(dir_path, exist_ok=True) + df.to_csv(log_name) + except: + pass + return df + + +def generate_ray_dataset(file_paths, dataloader_callable, lazy_mode=True, num_cpus=20): + decorated_dataloader_callable = get_failable_with_time(dataloader_callable) + if lazy_mode: + if num_cpus is None: + return ray.data.read_datasource(RayDataLoader(file_paths, decorated_dataloader_callable)) + else: + return ray.data.read_datasource( + RayDataLoader(file_paths, decorated_dataloader_callable), ray_remote_args={"num_cpus": num_cpus} + ) + else: + data = [] + for file in tqdm(file_paths, total=len(file_paths)): + content, error, elapse_time = decorated_dataloader_callable(file) + item = {"data": content, "filename": file, "error": error, "read_time": f"{elapse_time} secs"} + data.append(item) + return ray.data.from_items(data) + + +def ray_execute(ds, log_name): + with Timer(f"execute with Ray, status log: {log_name}"): + ret_with_status = ds.take_all() + df = save_logs(log_name, ret_with_status) + ret = df.to_dict(orient="records") + return ret + + +async def save_file_to_local_disk(save_path: str, file): + save_path = Path(save_path) + with save_path.open("wb") as fout: + try: + content = await file.read() + fout.write(content) + except Exception as e: + print(f"Write file failed. Exception: {e}") + raise HTTPException(status_code=500, detail=f"Write file {save_path} failed. Exception: {e}") + + +@timeout(seconds=TIMEOUT_SECONDS) +def data_to_redis_ray(data): + content = data["data"] + if content is None: + return { + "filename": data["filename"], + "content": content, + "status": "failed", + "num_chunks": -1, + "error": data["error"], + "read_time": data["read_time"], + "elaspe_time": "0.0 secs", + } + + decorated_callable = get_failable_with_time(data_to_redis) + num_chunks, error, elapse_time = decorated_callable(content) + status = "success" if not error else "failed" + if not debug: + content = None + return { + "filename": data["filename"], + "content": content, + "status": status, + "num_chunks": num_chunks, + "error": error, + "read_time": data["read_time"], + "elaspe_time": f"{elapse_time} secs", + } + + +def data_to_redis(data): + text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100, add_start_index=True) + chunks = text_splitter.split_text(data) + + # Create vectorstore + if tei_embedding_endpoint: + # create embeddings using TEI endpoint service + embedder = HuggingFaceHubEmbeddings(model=tei_embedding_endpoint) + else: + # create embeddings using local embedding model + embedder = HuggingFaceBgeEmbeddings(model_name=EMBED_MODEL) + + # Batch size + batch_size = 32 + num_chunks = len(chunks) + for i in range(0, num_chunks, batch_size): + batch_chunks = chunks[i : i + batch_size] + batch_texts = batch_chunks + + _ = Redis.from_texts( + texts=batch_texts, + embedding=embedder, + index_name=INDEX_NAME, + index_schema=INDEX_SCHEMA, + redis_url=REDIS_URL, + ) + # print(f"Processed batch {i//batch_size + 1}/{(num_chunks-1)//batch_size + 1}") + return num_chunks + + +class RayDataLoader(FileBasedDatasource): + def __init__( + self, + paths: Union[str, List[str]], + dataloader_callable: Optional[Callable], + document_ld_args: Optional[Dict[str, Any]] = None, + **file_based_datasource_kwargs, + ): + super().__init__(paths, **file_based_datasource_kwargs) + self.dataloader_callable = dataloader_callable + self.args = document_ld_args or {} + + def _read_stream(self, f: "pyarrow.NativeFile", path: str) -> Iterator[Block]: + from ray.data._internal.arrow_block import ArrowBlockBuilder + + builder = ArrowBlockBuilder() + path = f"{path}" + data, error, read_time = self.dataloader_callable(path) + item = {"data": data, "filename": path, "error": error, "read_time": f"{read_time} secs"} + builder.add(item) + yield builder.build() + + +def ingest_data_to_redis(file_list: List[DocPath], enable_ray=False, num_cpus=20): + """Ingest document to Redis.""" + file_list = [f.path for f in file_list] + + if enable_ray: + log_name = generate_log_name(file_list) + ds = generate_ray_dataset(file_list, document_loader, lazy_mode=True, num_cpus=num_cpus) + ds = ds.map(data_to_redis_ray, num_cpus=num_cpus) + return ray_execute(ds, log_name) + else: + for file in tqdm(file_list, total=len(file_list)): + with Timer(f"read document {file}."): + data = document_loader(file) + with Timer(f"ingest document {file} to Redis."): + data_to_redis(data) + return True + + +def ingest_link_to_redis(link_list: List[str], enable_ray=False, num_cpus=20): + link_list = [str(f) for f in link_list] + + def _parse_html(link): + data = parse_html([link]) + return data[0][0] + + if enable_ray: + log_name = generate_log_name(link_list) + ds = generate_ray_dataset(link_list, _parse_html, lazy_mode=True, num_cpus=num_cpus) + ds = ds.map(data_to_redis_ray, num_cpus=num_cpus) + return ray_execute(ds, log_name) + else: + for link in tqdm(link_list, total=len(link_list)): + with Timer(f"read document {link}."): + data = _parse_html(link) + if debug: + print("content is: ", data) + with Timer(f"ingest document {link} to Redis."): + data_to_redis(data) + return True + + +@register_microservice(name="opea_service@prepare_doc_redis", endpoint="/v1/dataprep", host="0.0.0.0", port=6007) +async def ingest_documents(files: List[UploadFile] = File(None), link_list: str = Form(None)): + if files and link_list: + raise HTTPException(status_code=400, detail="Provide either a file or a string list, not both.") + + if not files and not link_list: + raise HTTPException(status_code=400, detail="Must provide either a file or a string list.") + + saved_path_list = [] + if files: + try: + if not isinstance(files, list): + files = [files] + upload_folder = "./uploaded_files/" + if not os.path.exists(upload_folder): + Path(upload_folder).mkdir(parents=True, exist_ok=True) + + # TODO: use ray to parallelize the file saving + for file in files: + save_path = upload_folder + file.filename + await save_file_to_local_disk(save_path, file) + saved_path_list.append(DocPath(path=save_path)) + + if len(saved_path_list) <= 10: + enable_ray = False + else: + enable_ray = True + prepare_env(enable_ray=enable_ray) + num_cpus = get_max_cpus(len(saved_path_list)) + print(f"per task num_cpus: {num_cpus}") + ret = ingest_data_to_redis(saved_path_list, enable_ray=enable_ray, num_cpus=num_cpus) + return {"status": 200, "message": f"Data preparation succeeded. ret msg is {ret}"} + except Exception as e: + raise HTTPException(status_code=400, detail=f"An error occurred: {e}") + + if link_list: + try: + link_list = json.loads(link_list) # Parse JSON string to list + if not isinstance(link_list, list): + raise HTTPException(status_code=400, detail="link_list should be a list.") + if len(link_list) <= 10: + enable_ray = False + else: + enable_ray = True + prepare_env(enable_ray=enable_ray) + num_cpus = get_max_cpus(len(link_list)) + print(f"per task num_cpus: {num_cpus}") + ret = ingest_link_to_redis(link_list, enable_ray=enable_ray, num_cpus=num_cpus) + return {"status": 200, "message": f"Data preparation succeeded, ret msg is {ret}"} + except json.JSONDecodeError: + raise HTTPException(status_code=400, detail="Invalid JSON format for link_list.") + except Exception as e: + raise HTTPException(status_code=400, detail=f"An error occurred: {e}") + + +if __name__ == "__main__": + + opea_microservices["opea_service@prepare_doc_redis"].start() diff --git a/comps/dataprep/redis/langchain_ray/requirements.txt b/comps/dataprep/redis/langchain_ray/requirements.txt new file mode 100644 index 000000000..628454ff6 --- /dev/null +++ b/comps/dataprep/redis/langchain_ray/requirements.txt @@ -0,0 +1,22 @@ +beautifulsoup4 +docarray[full] +easyocr +fastapi +huggingface_hub +langchain +langchain-community +langsmith +numpy +opentelemetry-api +opentelemetry-exporter-otlp +opentelemetry-sdk +pandas +Pillow +pyarrow +pymupdf +python-docx +ray +redis +sentence_transformers +shortuuid +virtualenv diff --git a/comps/dataprep/redis/langchain_ray/schema_dim_768.yml b/comps/dataprep/redis/langchain_ray/schema_dim_768.yml new file mode 100644 index 000000000..adacf9865 --- /dev/null +++ b/comps/dataprep/redis/langchain_ray/schema_dim_768.yml @@ -0,0 +1,14 @@ +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +text: + - name: content + - name: source +numeric: + - name: start_index +vector: + - name: content_vector + algorithm: HNSW + datatype: FLOAT32 + dims: 768 + distance_metric: COSINE diff --git a/comps/dataprep/redis/test_data.pdf b/comps/dataprep/redis/test_data.pdf new file mode 100644 index 0000000000000000000000000000000000000000..db825848d1e248fdb801287a674ea29b9896dfa4 GIT binary patch literal 1083893 zcmdSB1yoht)-a3$(yf%lp(F%O2U5}?DIh3ahwkoBN>Y$e0YM1~l@d`Y6_Hj#R79jh zKomg{0sYrTeI6h6-uK@3`^PuN#~GWw_F8MtHP`Gp*8w3tWmO5JB!*6Ca_aFQ9W@e% zhaq4dHqLZ1GH@*?ccQhoQ!vpEhJpPjhnd2VXbc*LM&ixorCA0!U8(}KDG=tF|# ze(RHx`fVN-C58EQ9u|*7{Mv^@p|HR8;W5Zx`|v0vAOiIBH!gS-_O~_gXxwl3;jzfy z`=rpny-UDgf5U}9!2Py2DfI9AmBQkse!&lkK;V9bheTirzx5%d5Wn@I2*2$Oi6)?a zr2!Hnh4`%x`#X=2cpw=%o|;C`hGBn7|9 z5a`2VQ2$&P^x^Qo$Oh=c|4J9AkMQd}3<8V&MP9HN1cC6&-awz!Z#ZKBS$?4l7K23o z4iAI=m2X%K2LBsfFhJCPku@v^i^Bbe3l=EDFEqelalfw%ru_yF2blEByMO}tf3C4( z2vEQ95`jSCe%HYr%kYkaFg5J#V7_H+zLbz>n(_?G1e0z=}8oFjv$*zSiEp-%J#Oj#@}a zMOT%M`iJrQ=besv$5Q+-mr5`c1_@Vna`h#8!&P0aeTm9MTMs)TTmuHU0@otCJNP=n 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