text stringlengths 1 93.6k |
|---|
ports = host.find("ports")
|
if ports:
|
for port in ports.findall("port"):
|
cells = []
|
for rc in row_cells:
|
current_cell = rc
|
for bc in re.findall("(\[[a-z\.*]+\])", rc):
|
for definition in definitions:
|
elem = definition.find(bc[1:-1])
|
if elem:
|
xml_element = port.find(elem.xpathfull())
|
if xml_element is not None:
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data = elem.data(xml_element)
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current_cell = current_cell.replace(bc, data)
|
break
|
break
|
cells.append(current_cell)
|
port_info.append(cells)
|
result[address] = port_info
|
# Start converting data to Markdown
|
# IP addresses are defined as a header
|
for address in result:
|
if not options.print_empty and len(result[address]) == 0:
|
continue
|
if options.hs != 0:
|
md += "%s %s\n\n" % ('#' * options.hs, address)
|
md += "| %s |" % " | ".join(columns)
|
md += "\n"
|
# Adding +2 for 1 space on left and right sides
|
md += "|%s|" % "|".join(map(lambda s: '-' * (len(s) + 2), columns))
|
md += "\n"
|
result[address] = sorted(
|
result[address],
|
key=lambda row: row[sorting_index],
|
reverse=sorting_reverse
|
)
|
for port_info in result[address]:
|
md += "| %s |" % " | ".join(port_info)
|
md += "\n"
|
md += "\n\n"
|
print()
|
print()
|
print(md)
|
# <FILESEP>
|
from transformers import (AutoTokenizer, AutoConfig, LlamaForCausalLM, DataCollatorForLanguageModeling, Trainer, TrainingArguments)
|
from datasets import load_dataset
|
from huggingface_hub import login
|
import wandb
|
from utils import *
|
### Login
|
# Wandb is for logging and is optional.
|
hf_token = "<your_hf_token>"
|
wb_token = "<your_wb_token>"
|
wandb.login(key=wb_token)
|
login(token=hf_token)
|
### Load and tokenize training data. Uncomment these lines to load and tokenize yourself.
|
# data_source = "Skylion007/openwebtext"
|
# data = load_dataset(data_source)
|
# subset = load_dataset(data_source, split="train[:15%]")
|
# context_length = 256
|
# tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
|
# def tokenize(element):
|
# outputs = tokenizer(
|
# element["text"],
|
# truncation=False,
|
# max_length=context_length,
|
# return_overflowing_tokens=True,
|
# return_length=True,
|
# )
|
# # Combine all tokens
|
# combined = []
|
# for tokenized_doc in outputs['input_ids']:
|
# combined += tokenized_doc + [tokenizer.eos_token_id]
|
# # Chunk
|
# input_batch = []
|
# for i in range(0, len(combined) - context_length, context_length):
|
# input_batch.append(combined[i:i+context_length])
|
# return {"input_ids": input_batch}
|
# tokenized_data = subset.map(
|
# tokenize, batched=True, remove_columns=data["train"].column_names,
|
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