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import argparse
import gzip
import json
from pathlib import Path
from typing import (
Callable,
Dict,
Iterable,
Iterator,
List,
Optional,
Sequence,
TextIO,
Tuple,
Union,
)
import datasets
import numpy as np
import pandas as pd
TO_REMOVE = [
"meta",
"perplexity_score",
"text_length",
"url",
"domain",
"dup_ratio",
"pairs",
"repetitions",
"included_in_dedup",
"cluster",
"id",
]
L_TO_NAME = {
"en": "English",
"de": "German",
"fr": "French",
"es": "Spanish",
"it": "Italian",
"ru": "Russian",
"zh": "Chinese",
"ko": "Korean",
"pt": "Portuguese",
"nl": "Dutch",
"pl": "Polish",
"sv": "Swedish",
}
def gen(l):
for x in l:
yield x
def _close_when_exhausted(file: TextIO) -> Iterable[str]:
with file:
for line in file:
yield json.loads(line)
def _close_when_exhausted_txt(file: TextIO) -> Iterable[str]:
with file:
for line in file:
yield line[:-1] # ignore new line
def open_read_cleaned(filename) -> Iterable[str]:
file: TextIO = gzip.open(filename, "rt") # type: ignore
return _close_when_exhausted(file)
def open_gzip_txt(filename) -> Iterable[str]:
file: TextIO = gzip.open(filename, "rt") # type: ignore
return _close_when_exhausted_txt(file)
def read_parallel_corpus(dir: str, lp: str) -> Tuple[Iterable[str], Iterable[str]]:
src_l, tgt_l = lp.split("-")
if src_l != "en":
lp_path = f"{tgt_l}-{src_l}"
else:
lp_path = lp
src_path = Path(dir) / f"cometkiwi_data.{lp_path}.{src_l}"
tgt_path = Path(dir) / f"cometkiwi_data.{lp_path}.{tgt_l}"
src_corpus = open_gzip_txt(src_path)
tgt_corpus = open_gzip_txt(tgt_path)
return src_corpus, tgt_corpus
def unroll_chat(chat):
chat_str = ""
for i, turn in enumerate(chat):
if type(turn["value"]) != str:
pass
else:
chat_str += turn["value"]
return chat_str
parser = argparse.ArgumentParser()
parser.add_argument("--dataset_path", type=str, required=True)
parser.add_argument("--output", type=str, required=True)
parser.add_argument("--is_hf_dataset", type=str, required=True, default=False)
parser.add_argument("--n_tokens", type=int, required=False, default=None)
parser.add_argument("--threshold", type=int, required=False, default=None)
parser.add_argument("--min_perplexity", type=int, required=False, default=None)
parser.add_argument("--wikipedia", type=str, required=False, default=False)
parser.add_argument("--posterior_tokens", type=str, required=False, default=False)
parser.add_argument("--n_posterior_tokens", type=int, required=False, default=None)
parser.add_argument("--is_parallel", type=str, required=False, default=False)
parser.add_argument("--lp", type=str, required=False)
args = parser.parse_args()
if args.posterior_tokens == "False":
if args.wikipedia == "True":
print("on wikipedia")
data = []
dataset_paths = [p for p in Path(args.dataset_path).iterdir()]
dfs = []
for dataset_path in dataset_paths:
print("on path", dataset_path)
corpus = open_read_cleaned(dataset_path)
for doc in corpus:
data.append({"text": doc["text"]})
print(dataset_path)
sub_df = pd.DataFrame(data=data)
dfs.append(sub_df)
df = pd.concat(dfs, ignore_index=True)
dataset = datasets.Dataset.from_pandas(df)
dataset.to_json(args.output, lines=True)
else:
if args.is_hf_dataset == "True":
if args.dataset_path == "Unbabel/TowerBlocks-v0.1":
df = datasets.load_dataset(
"Unbabel/TowerBlocks-v0.1", split="train"
).to_pandas()
dataset = pd.DataFrame()
dataset["text"] = df["conversations"].apply(unroll_chat)
dataset = datasets.Dataset.from_pandas(dataset)
else:
dataset = datasets.load_from_disk(args.dataset_path)
instances_to_select = []
n_words = 0
for idx in range(len(dataset)):
perplexity = dataset[int(idx)]["perplexity_score"]
if perplexity < args.threshold and perplexity > args.min_perplexity:
instances_to_select.append(idx)
n_words += len(dataset[int(idx)]["text"].split(" "))
print(f"Selected {n_words} of {args.n_tokens} tokens.")
if n_words >= args.n_tokens:
break
dataset = dataset.select(instances_to_select)
# Remove columns if they exist
for column in TO_REMOVE:
if column in dataset.column_names:
dataset = dataset.remove_columns(column)
print("English")
print("n words", n_words)
elif args.is_parallel == "False":
data = []
corpus = open_read_cleaned(args.dataset_path)
n_words = 0
for doc in corpus:
perplexity = doc["perplexity"]
if perplexity < args.threshold and perplexity > args.min_perplexity:
if args.lp == "zh":
n_words += len(doc["text"])
else:
n_words += len(doc["text"].split(" "))
data.append({"text": doc["text"]})
if n_words >= args.n_tokens:
break
print(args.dataset_path)
print("n words", n_words)
dataset = datasets.Dataset.from_pandas(pd.DataFrame(data=data))
elif args.is_parallel == "True":
data = []
src_data, tgt_data = read_parallel_corpus(
dir=f"{args.dataset_path}", lp=args.lp
)
n_sents = 0
for src, tgt in zip(src_data, tgt_data):
if n_sents >= args.n_tokens:
break
data.append(
{
"text": f"{L_TO_NAME[args.lp.split('-')[0]]}: {src}\n{L_TO_NAME[args.lp.split('-')[-1]]}: {tgt}"
}
)
n_sents += 1
if n_sents % 1000 == 0:
print(f"Selected {n_sents} of {args.n_tokens} sentences.")
data_len = len(data)
# if xx-en, take 1st half of data; otherwise, take 2nd half
if "-en" in args.lp:
data = data[: int(data_len / 2)]
else:
data = data[int(data_len / 2) :]
dataset = datasets.Dataset.from_pandas(pd.DataFrame(data=data))
dataset.to_json(args.output, lines=True)
else:
if args.is_hf_dataset:
dataset = datasets.load_from_disk(args.dataset_path)
instances_to_select = []
n_words = 0
surpassed = False
for idx in range(len(dataset)):
perplexity = dataset[int(idx)]["perplexity_score"]
if perplexity < args.threshold and perplexity > args.min_perplexity:
n_words += len(dataset[int(idx)]["text"].split(" "))
if n_words >= args.n_tokens:
if surpassed:
instances_to_select.append(idx)
n_posterior_words += len(dataset[int(idx)]["text"].split(" "))
if n_posterior_words >= args.n_posterior_tokens:
break
else:
n_posterior_words = 0
surpassed = True
dataset = dataset.select(instances_to_select)
# Remove columns if they exist
for column in TO_REMOVE:
if column in dataset.column_names:
dataset = dataset.remove_columns(column)
print("English")
print("n words", n_words)
# here, we only start appending after the n_words threshold is satisfied once (this should be connected to another run)
else:
data = []
corpus = open_read_cleaned(args.dataset_path)
n_words = 0
surpassed = False
for doc in corpus:
perplexity = doc["perplexity"]
if perplexity < args.threshold and perplexity > args.min_perplexity:
n_words += len(doc["text"].split(" "))
# once we surpass the number of tokens, start appending on the next iteration
if n_words >= args.n_tokens:
if surpassed:
data.append({"text": doc["text"]})
n_posterior_words += len(doc["text"].split(" "))
if n_posterior_words >= args.n_posterior_tokens:
break
if not surpassed:
n_posterior_words = 0
surpassed = True
print(args.dataset_path)
print("n words", n_words)
dataset = datasets.Dataset.from_pandas(pd.DataFrame(data=data))
dataset.to_json(args.output, lines=True)
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