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)