text stringlengths 1 93.6k |
|---|
path=args.input_path,
|
global_rank=args.global_rank,
|
world_size=args.world_size,
|
)
|
eval_dataset = src.data.Dataset(
|
data=eval_examples,
|
num_tests=args.num_tests_input,
|
num_examples=args.num_examples,
|
fewshot_method=args.fewshot_method,
|
function_name=args.function_name,
|
strip_prompt=args.strip_prompt,
|
)
|
eval_sampler = SequentialSampler(eval_dataset)
|
tokenization_kwargs = {
|
"max_length": args.max_length_input,
|
"truncation": True,
|
"padding": True,
|
"return_tensors": "pt",
|
}
|
collator_function = src.data.Collator(tokenizer, **tokenization_kwargs)
|
eval_dataloader = DataLoader(
|
eval_dataset,
|
sampler=eval_sampler,
|
batch_size=args.per_gpu_batch_size,
|
num_workers=args.world_size,
|
collate_fn=collator_function,
|
)
|
# with init_empty_weights():
|
# config = AutoConfig.from_pretrained(args.model_name_or_path)
|
# model = AutoModelForCausalLM.from_config(config)
|
model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path, **model_kwargs)
|
logger.info(f"Model Using Device: {model.device}")
|
if args.world_size <= 1:
|
model = model.to(args.device)
|
logger.info(f"Model Using Device: {model.device}")
|
logger.info("Start eval")
|
scores_dict = evaluate(model, eval_dataloader, tokenizer, args)
|
logger.info(f"Scores: {scores_dict}")
|
if args.is_main:
|
glob_path = Path(args.output_dir) / f"{args.language}-{args.model_size}-{args.model_data}-predictions"
|
write_path = args.output_path
|
src.utils.write_output(glob_path, write_path)
|
if __name__ == "__main__":
|
parser = src.config.Arguments()
|
parser.add_eval_args()
|
args = parser.parse()
|
src.slurm.init_distributed_mode(args)
|
src.slurm.init_signal_handler()
|
if args.is_distributed:
|
torch.distributed.barrier()
|
logger = src.utils.init_logger(
|
args.is_main, args.is_distributed,
|
Path(args.output_dir) / "run.log"
|
)
|
if not Path(args.output_dir).exists() and args.is_main:
|
parser.print_options(args)
|
main()
|
# <FILESEP>
|
#!/usr/bin/env python3
|
# -*- coding: utf-8 -*
|
'''
|
项目名称: JD-Script / jd_ccfxj_help
|
Author: Curtin
|
功能：城城分现金助力 活动入口：15.0:/￥WAAD60EE92byb0%，☃そ點①點ひ领哯唫！
|
Date: 2021/10/20 下午8:59
|
TG交流 https://t.me/topstyle996
|
TG频道 https://t.me/TopStyle2021
|
说明：仅测试使用，目前只助力，需要手动领取提现。
|
cron: 0 0 9-21 1 *
|
new Env('城城分现金助力(1.9-1.21)');
|
update 2022.1.9
|
'''
|
## 助力账号名称：可填用户名 或 pin的值不要; env 设置 export ccfxj_help="Curtinlv&用户2" 多账号&分隔
|
ccfxj_help=["Curtinlv", ]
|
#是否开启通知，Ture：发送通知，False：不发送
|
isNotice="true"
|
# UA 可自定义你的，注意格式: 【 jdapp;iPhone;10.0.4;14.2;9fb54498b32e17dfc5717744b5eaecda8366223c;network/wifi;ADID/2CF597D0-10D8-4DF8-C5A2-61FD79AC8035;model/iPhone11,1;addressid/7785283669;appBuild/167707;jdSupportDarkMode/0;Mozilla/5.0 (iPhone; CPU iPhone OS 14_2 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Mobile/null;supportJDSHWK/1 】
|
UserAgent = ''
|
ccfxj_isOrder="true"
|
countM = {}
|
outCK = []
|
import os, re, sys
|
import random
|
try:
|
import requests
|
except Exception as e:
|
print(e, "\n缺少requests 模块，请执行命令安装：python3 -m pip install requests")
|
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