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def get_train_overlap(docs_by_task_set: dict, ngrams_path: str, limit: int) -> dict:
info_dict_path = os.path.join(ngrams_path, 'info.json')
info_dict = json.load(open(info_dict_path, 'r', encoding='utf-8'))
ngrams_n_size = info_dict['ngram_size']
janitor = Janitor()
print('Building Lookups...')
start = time.perf_counter()
def get_overlaps_dump_path(task_name, task_set, ngrams_n_size, limit) -> str:
return f'data/{task_name}/{task_set}_{ngrams_n_size}grams_limit{limit}.overlaps'
lookups = {}
duplicates = {}
sets_to_decontaminate = len(docs_by_task_set.keys())
for ((task_name, task_set), docs) in docs_by_task_set.items():
if not os.path.exists(f'data/{task_name}'):
os.mkdir(f'data/{task_name}')
overlaps_dump_path = get_overlaps_dump_path(task_name, task_set, ngrams_n_size, limit)
if os.path.exists(overlaps_dump_path):
duplicates[task_name, task_set] = pickle.load(open(overlaps_dump_path, 'rb'))
sets_to_decontaminate -= 1
continue
else:
duplicates[task_name, task_set] = set()
task_set_lookup_path = f'data/{task_name}/{task_set}_{ngrams_n_size}grams_limit{limit}.lookup'
if os.path.exists(task_set_lookup_path):
print(f'{task_set_lookup_path} available, loading...')
lookups[task_name, task_set] = pickle.load(open(task_set_lookup_path, 'rb'))
else:
print(f'{task_set_lookup_path} not available, building...')
lookup = collections.defaultdict(set)
for (doc_id, document) in enumerate(docs):
ngrams = word_ngrams(janitor.normalize_string(document), ngrams_n_size)
for ngram in ngrams:
lookup[ngram].add(doc_id)
pickle.dump(lookup, open(task_set_lookup_path, 'wb'))
lookups[task_name, task_set] = lookup
elapsed = time.perf_counter() - start
print(f'Building lookups took {elapsed:0.5f} seconds.')
matched_ngrams = []
if sets_to_decontaminate > 0:
print('Merging lookups...')
start = time.perf_counter()
merged_lookup = collections.defaultdict(list)
for ((task_name, task_set), lookup) in lookups.items():
for (ngram, doc_ids) in lookup.items():
merged_lookup[ngram].append((task_name, task_set, doc_ids))
elapsed = time.perf_counter() - start
print(f'Merging lookups took {elapsed:0.5f} seconds.')
print(f'{ngrams_n_size} grams files found in {ngrams_path}:')
files = glob.glob(os.path.join(ngrams_path, '*.sorted.zst'))
print(files)
for file in files:
start = time.perf_counter()
print(f'Scanning {file}')
reader = ZStdTextReader(file)
total_ngrams = 0
unique_ngrams = 0
matching_unique = 0
non_matching_unique = 0
current_ngram = ''
for line in reader.read_tqdm():
total_ngrams += 1
[ngram, document_id] = line.rsplit(' ', 1)
if ngram != current_ngram:
unique_ngrams += 1
current_ngram = ngram
if ngram in merged_lookup:
matched_ngrams.append(ngram)
matching_unique += 1
for (task_name, task_set, doc_ids) in merged_lookup[ngram]:
task_doc_set = duplicates[task_name, task_set]
for doc_id in doc_ids:
task_doc_set.add(doc_id)
del merged_lookup[ngram]
else:
non_matching_unique += 1
print(f'Total Ngrams: {total_ngrams}')
print(f'Unique Ngrams: {unique_ngrams}')
print(f'Unique Matching: {matching_unique}')
print(f'Unique Non Matching: {non_matching_unique}')
print('Matched ngrams:')
for ngram in matched_ngrams:
print(ngram)
elapsed = time.perf_counter() - start
print(f'Read took {elapsed:0.5f} seconds.')
print(f'Speed: {os.path.getsize(file) / 1000000.0 / elapsed}MB/second')
print(duplicates)
for ((task_name, task_set), doc_ids) in duplicates.items():
overlaps_dump_path = get_overlaps_dump_path(task_name, task_set, ngrams_n_size, limit)
pickle.dump(doc_ids, open(overlaps_dump_path, 'wb'))
return {task_name: doc_ids for ((task_name, task_set), doc_ids) in duplicates.items()}
# File: lm-evaluation-harness-main/lm_eval/decontamination/janitor.py
import pickle
import re
import string
import traceback
from typing import Iterator, List, Sequence, Tuple, TypeVar
try:
import janitor_util