def unambiguize(dir):
root = "/Users/alexwarstadt/Workspace/data_generation/outputs/inductive_biases/"
dir_path = os.path.join(root, dir)
train_file = jsonlines.open(os.path.join(dir_path, "train.jsonl"))
train_control_file = jsonlines.open(
os.path.join(dir_path, "control_train.jsonl"))
train_pairs = read_pairs(train_file)
train_control_pairs = read_pairs(train_control_file)
percents = [0.0026, 0.005, 0.01, 0.02, 0.04, 0.08, 0.16]
for p in percents:
data = random.sample(train_pairs, int(5000.0 * (1 - p)))
data.extend(random.sample(train_control_pairs, int(5000 * p)))
output_file = open(os.path.join(dir_path, "train_%s.jsonl" % str(p)),
"w")
w = jsonlines.Writer(output_file)
data = unzip_pairs(data)
w.write_all(data)
w.close()
test_file = jsonlines.open(os.path.join(dir_path, "test.jsonl"))
test_control_file = jsonlines.open(
os.path.join(dir_path, "control_test.jsonl"))
test_data = [x for x in test_file]
test_data.extend([x for x in test_control_file])
output_file = open(os.path.join(dir_path, "test_combined.jsonl"), "w")
w = jsonlines.Writer(output_file)
w.write_all(test_data)
w.close()
def __init__(
self,
owner,
filename: str,
format: str = "jsonl",
compression: Optional[str] = None,
**kwargs: Any,
):
super().__init__(owner)
dirname = os.path.abspath(os.path.dirname(filename))
basename = os.path.basename(filename)
nodes_filename = os.path.join(dirname if dirname else "",
f"{basename}_nodes.{format}")
edges_filename = os.path.join(dirname if dirname else "",
f"{basename}_edges.{format}")
if dirname:
os.makedirs(dirname, exist_ok=True)
if compression == "gz":
nodes_filename += f".{compression}"
edges_filename += f".{compression}"
NFH = gzip.open(nodes_filename, "wb")
self.NFH = jsonlines.Writer(NFH)
EFH = gzip.open(edges_filename, "wb")
self.EFH = jsonlines.Writer(EFH)
else:
self.NFH = jsonlines.open(nodes_filename, "w")
self.EFH = jsonlines.open(edges_filename, "w")
def generate_paradigm(self,
number_to_generate=1000,
rel_output_path=None,
absolute_path=None):
"""
Contains the main loop for generating a full dataset for a given paradigm.
Also contains exception handling: some exceptions are tolerated because sometimes no matching arguments can be found,
but if at least 10% of cases have an exception, it terminates since this is probably an issue in the code, and
it could cause an infinite loop otherwise.
:param number_to_generate: number of minimal pairs/sets to generate
:param rel_output_path: relative path of output file
:param absolute_path: absolute path of output file
:return: None
"""
if rel_output_path is not None:
project_root = "/".join(
os.path.join(os.path.dirname(
os.path.abspath(__file__))).split("/")[:-1])
output = open(os.path.join(project_root, rel_output_path), "w")
elif absolute_path is not None:
output = open(absolute_path, "w")
else:
raise Exception("You need to give an output path")
past_sentences = set()
generated_data = []
pairID = 0
error_counter = 0
constant_data = self.make_metadata_dict()
print("Generating data for " + constant_data["UID"])
self.make_logger(constant_data)
output_writer = jsonlines.Writer(output, flush=True)
while len(past_sentences) < number_to_generate:
try:
new_data, track_sentence = self.sample()
if track_sentence not in past_sentences:
past_sentences.add(track_sentence)
for field in self.data_fields:
if field in new_data:
new_data[field] = string_beautify(new_data[field])
new_data.update(constant_data)
new_data["pairID"] = str(pairID)
pairID += 1
if pairID % 100 == 0:
print("%d sentences generated" % pairID)
output_writer.write(new_data)
except Exception as e:
self.log_exception(e)
print(self.get_stack_trace(e))
error_counter += 1
if error_counter > number_to_generate // 5:
pass
# raise Exception("Over 20\% of samples result in errors. You should fix this.")
jsonlines.Writer(output).write_all(generated_data)
def execute(self, context):
"""
Executed by task_instance at runtime
"""
mongo_conn = MongoHook(self.mongo_conn_id).get_conn()
s3_conn = S3Hook(self.s3_conn_id)
# Grab collection and execute query according to whether or not it is a pipeline
collection = mongo_conn.get_database(self.mongo_db).get_collection(
self.mongo_collection)
results = collection.aggregate(
self.mongo_query) if self.is_pipeline else collection.find(
self.mongo_query)
# Performs transform then stringifies the docs results into json format
# docs_str = self._stringify(self.transform(results))
tmp_file = NamedTemporaryFile()
print("writing results to temp file")
start = datetime.now()
with jsonlines.Writer(tmp_file) as writer:
writer.write_all(results)
tmp_file.close()
end = datetime.now()
print("took %i seconds" % (end - start).total_seconds())
s3_conn.load_file(tmp_file.name,
self.s3_key,
bucket_name=self.s3_bucket,
replace=self.replace)
os.unlink(tmp_file.name)
def main():
parser = argparse.ArgumentParser(prog="examples-to-dataset.py")
parser.add_argument("examples_directory",
help="Directory with WAV files and JSON intents")
parser.add_argument("output_file", help="jsonl output file")
parser.add_argument(
"--dataset-directory",
default=os.getcwd(),
help="Base directory of dataset (default: cwd)",
)
args = parser.parse_args()
examples_dir = Path(args.examples_directory)
dataset_dir = Path(args.dataset_directory)
with open(args.output_file, "w") as output_file:
with jsonlines.Writer(output_file) as out:
for wav_path in examples_dir.glob("*.wav"):
json_path = wav_path.with_suffix(".json")
if not json_path.is_file():
continue
wav_name = str(wav_path.relative_to(dataset_dir))
with open(json_path, "r") as json_file:
intent = json.load(json_file)
intent["wav_name"] = wav_name
out.write(intent)
def myfilter():
funs0 = {'Concat', 'Length', 'Substr', 'Index', 'Replace'}
funs1 = {'ToLower', 'ToUpper', 'Trim', 'TrimEnd', 'TrimStart'}
infile = open('./__exprs.jsonl', mode='r')
outfile = open(input('Enter output file name: '), mode='w')
reader = jsonlines.Reader(infile)
writer = jsonlines.Writer(outfile)
pred = lambda x: len(x['text']) < 300 and \
allin(x['functions'], funs0.union(funs1)) and \
oneof(x['functions'], funs0) and \
oneof(x['functions'], funs1)
filtered = dict((x['text'], x) for x in reader if pred(x)).values()
sorted_ = sorted(filtered, key=lambda x: len(x['functions']), reverse=True)
writer.write_all(sorted_)
# slice = itertools.islice(filtered, 10)
# print(list(slice))
reader.close()
infile.close()
writer.close()
outfile.close()
def query_all_training_data(self):
"""
Iterate through training data, query and write to file
:return:
"""
# open data file and read lines
with open(self.data_path, 'r') as f:
for line in tqdm.tqdm(f):
try:
dataline = json.loads(line)
s_ent = dataline['source_ent']
t_ent = dataline['target_ent']
label = dataline['label']
s_ent_new = self.update_json_ent(s_ent)
t_ent_new = self.update_json_ent(t_ent)
line = {
'source_ent': s_ent_new,
'target_ent': t_ent_new,
'label': label
}
with jsonlines.Writer(open(self.out_path, 'a')) as writer:
writer.write(line)
except Exception:
continue
return
def save_documents(jsonl_out: pathlib.Path,
documents: t.Iterator[dict]) -> None:
"""
Saves the documents to a `JSONL` file
Parameters
----------
jsonl_out : pathlib.Path
The JSONL file to contain all the documents
documents : Iterator[dict]
The JSON documents to save
"""
bar_i = 0
widgets = [
'Saving JSONL # ',
pb.Counter(), ' ',
pb.Timer(), ' ',
pb.BouncingBar(marker='.', left='[', right=']')
]
with pb.ProgressBar(widgets=widgets) as bar:
with open(jsonl_out, 'w', encoding='utf-8') as fp:
with jl.Writer(fp, compact=True, sort_keys=True) as writer:
for document in documents:
writer.write(document)
bar_i = bar_i + 1
bar.update(bar_i)
def write_jsonls(file_path, data, log=log):
"Sream Write to JSON Lines. 'data' must be namedtuple. schema is a dict of field to data-type"
import jsonlines
s_t = now()
l_t = now()
msg_dlt = 10000
counter = 0
counter2 = 0
with open(file_path, 'wb') as f:
w = jsonlines.Writer(f)
for row in data:
w.write(row)
counter += 1
counter2 += 1
# progress message
if counter2 % msg_dlt == 0:
secs_l = (now() - l_t).total_seconds()
if secs_l >= 20:
secs = (now() - s_t).total_seconds()
rate = round(counter2 / secs_l, 1)
mins = round(secs / 60, 1)
log("{} min ## Writing to JSON: {} rows @ {} r/s.".format(
mins, counter, rate))
l_t = now()
counter2 = 0
secs = (now() - s_t).total_seconds()
rate = round(counter / secs, 1)
log("Wrote {} rows to {} [{} r/s].".format(counter, file_path, rate))
def filter_dataset_parallel(path: str, out_path: str):
full_path = pathlib.Path(path).resolve()
read_f = gzip.open(full_path, "rb") if path.endswith(".jsonl.gz") else full_path.open("r")
reader = jsonlines.Reader(read_f)
full_out_path = pathlib.Path(out_path).resolve()
write_f = gzip.open(full_out_path, "wb") if out_path.endswith(".jsonl.gz") else full_out_path.open("w")
writer = jsonlines.Writer(write_f)
logger.debug(f"Writing output to {full_out_path}...")
examples = []
logger.debug(f"Loading {full_path}")
pool = Pool(processes=16)
total_lines = 0
num_written = 0
for has_error, json_dict in tqdm.tqdm(pool.imap(has_transform_error, reader, chunksize=4), desc=full_path.name):
total_lines += 1
if not has_error:
writer.write(json_dict)
num_written += 1
if total_lines % 1000 == 0:
logger.debug(f"Filtered jsonl to {num_written}/{total_lines}")
logger.debug(f"DONE: Filtered jsonl to {num_written}/{total_lines}")
read_f.close()
write_f.close()
def write_jsonl(entities: List[dict],
fp: Union[BinaryIO, TextIO],
max_size: int = None,
default=None):
"""
Converts the provided list of dicts to jsonl and writes to the file handle
:param entities: The list of dicts to be converted to jsonl
:param fp: The file handle to which the output should be written
:param max_size: A per-line size limit - jsonl lines over this size will be dropped from the output
:param default: A function that should return a serializable version of the dict
"""
def bytes_serializer(obj):
if isinstance(obj, bytes):
return obj.decode('utf8', errors='backslashreplace')
elif isinstance(obj, dict):
return json.dumps(obj, separators=(',', ':'), default=default)
raise TypeError(repr(obj) + ' is not JSONL serializable')
# Convert the result dictionary of metadata into serialized bytes and then store them
with jsonlines.Writer(fp, compact=True, flush=True,
dumps=bytes_serializer) as writer:
for obj in entities:
# Verify that the object isn't over a maximum size limit
if max_size is not None:
obj_size = get_size(obj)
if obj_size > max_size:
logger.warning(
f'Dropping result that is over size limit ({obj_size} bytes > {max_size} byte limit)'
)
continue
writer.write(obj)
def do_phonemize(args):
"""
Reads JSONL from stdin with "clean_words" property.
Looks up or guesses phonetic pronuncation(s) for all clean words.
Prints a line of JSON for each input line.
"""
from .lang import get_phonemizer
from .commands import phonemize
word_break = IPA.BREAK_WORD if args.word_breaks else None
phonemizer = get_phonemizer(
args.language,
args.lang_dir,
use_word_indexes=args.word_indexes,
word_break=word_break,
no_g2p=args.no_g2p,
)
if os.isatty(sys.stdin.fileno()):
print("Reading tokenize JSONL from stdin...", file=sys.stderr)
def sentence_generator():
for line in sys.stdin:
line = line.strip()
if not line:
continue
yield json.loads(line)
writer = jsonlines.Writer(sys.stdout, flush=True)
for utt_json in phonemize(phonemizer, sentence_generator()):
writer.write(utt_json)
def yaml2nedb(yamlfile):
output = []
with open(yamlfile, "r") as reader:
for obj in yaml.safe_load_all(reader):
output.append(obj)
jsonwriter = jsonlines.Writer(sys.stdout, compact=True)
jsonwriter.write_all(output)
def send_event(topic, payload_dict={}):
print(topic, end=" ", file=events_out_file)
with jsonlines.Writer(events_out_file) as out:
out.write(payload_dict)
events_out_file.flush()
def do_tokenize(args):
"""
Split lines from stdin into sentences, tokenize and clean.
Prints a line of JSON for each sentence.
"""
from .commands import tokenize
from .lang import get_tokenizer
tokenizer = get_tokenizer(args.language, lang_dir=args.lang_dir, no_pos=args.no_pos)
if args.text:
# Use arguments
lines = args.text
else:
# Use stdin
lines = sys.stdin
if os.isatty(sys.stdin.fileno()):
print("Reading text from stdin...", file=sys.stderr)
writer = jsonlines.Writer(sys.stdout, flush=True)
for sent_json in tokenize(
tokenizer,
lines,
is_csv=args.csv,
csv_delimiter=args.csv_delimiter,
split_sentences=args.split_sentences,
):
writer.write(sent_json)
def main(path_corpus_root: str, path_processed_jsonl: str,
path_morph_freq_file: str):
__morphs = []
fp = open(path_processed_jsonl, 'w')
writer = jsonlines.Writer(fp)
for root, subdirs, files in os.walk(path_corpus_root):
logging.info('processing {}...'.format(files))
if len(subdirs) > 0:
continue
for f_name in tqdm.tqdm(files):
__root, dir_name = os.path.split(root)
path_text_file = os.path.join(root, f_name)
processed_obj = load_liverdoor_corpus(path_text_file, f_name,
dir_name)
writer.write(processed_obj)
__morphs += processed_obj['morphs']
else:
pass
else:
writer.close()
fp.close()
morphs_freq = [(k, v) for k, v in Counter(__morphs).items()]
fp = open(path_morph_freq_file, 'w')
for t in morphs_freq:
fp.write('{} {}'.format(t[0], t[1]) + '\n')
else:
fp.close()
def do_phones_to_phonemes(config, args):
"""Transform/group phones in a pronuncation into language phonemes"""
phonemes_path = Path(pydash.get(config, "language.phonemes"))
with open(phonemes_path, "r") as phonemes_file:
phonemes = gruut_ipa.Phonemes.from_text(phonemes_file)
keep_stress = pydash.get(config, "language.keep_stress", False)
if args.phones:
phones = args.phones
else:
# Read from stdin
phones = sys.stdin
if os.isatty(sys.stdin.fileno()):
print("Reading pronunciations from stdin...", file=sys.stderr)
writer = jsonlines.Writer(sys.stdout, flush=True)
for line in phones:
line = line.strip()
if line:
line_phonemes = phonemes.split(line, keep_stress=keep_stress)
phonemes_list = [p.text for p in line_phonemes]
writer.write({
"language": args.language,
"raw_text": line,
"phonemes_text": " ".join(phonemes_list),
"phonemes_list": phonemes_list,
"phonemes": [p.to_dict() for p in line_phonemes],
})
def do_print_phoneme_counts(config, args):
"""
Print counts of all phonemes from the lexicon.
"""
gruut_lang = try_load_language(args)
writer = jsonlines.Writer(sys.stdout, flush=True)
phoneme_counts = Counter()
for phoneme in gruut_lang.phonemes:
phoneme_counts[phoneme.text] = 0
for word_prons in gruut_lang.phonemizer.lexicon.values():
for word_pron in word_prons:
for phoneme in word_pron:
if not phoneme:
continue
while phoneme and (gruut_ipa.IPA.is_stress(phoneme[0])
or gruut_ipa.IPA.is_accent(phoneme[0])):
phoneme = phoneme[1:]
if phoneme:
phoneme_counts[phoneme] += 1
writer.write(phoneme_counts.most_common())
def main():
parser = argparse.ArgumentParser("vocab_g2p")
parser.add_argument("--output",
default=None,
help="Path to write guesses as JSON (default: stdout)")
parser.add_argument("--debug",
action="store_true",
help="Print DEBUG messages to console")
args, _ = parser.parse_known_args()
# -------------------------------------------------------------------------
if args.debug:
logging.basicConfig(level=logging.DEBUG)
logger.debug(args)
output_file = sys.stdout
if args.output:
output_file = open(args.output, "w")
pronunciations = defaultdict(list)
for line in sys.stdin:
word, phonemes = re.split(r"\s+", line.strip(), maxsplit=1)
pronunciations[word].append(phonemes)
with jsonlines.Writer(output_file) as out:
out.write(pronunciations)
def generate(gpt2, split, out_dir, start=None, end=None):
print('Getting prefixes')
id_prefixs = get_prefixs(split)
if (start is not None) and (end is not None):
id_prefixs = id_prefixs[start:end]
print(len(id_prefixs))
# get out path
if not os.path.exists(out_dir):
os.mkdir(out_dir)
out_fn = '{}'.format(split)
if (start is not None) and (end is not None):
out_fn += '_{}-{}'.format(start, end)
out_fn += '.jsonl'
out_fp = os.path.join(out_dir, out_fn)
print(out_fp)
print('Generating text')
output = []
step = 10
writer = jsonlines.Writer(open(out_fp, 'w'), flush=True)
for i in range(0, len(id_prefixs), step):
print('GENERATED: ', i)
cur_ids, cur_prefixs = zip(*id_prefixs[i:i+step])
cur_gentexts = gpt2.generate_conditional(prompts=cur_prefixs)
# add to batch
cur_batch = [{'id': cur_ids[j], 'text': cur_gentexts[j]} for j in range(len(cur_ids))]
for item in cur_batch:
writer.write(item)
def write(self, tweets: Union[Any, List[Any]]) -> None:
"""Write Tweet or list of Tweets to file"""
with jl.Writer(self.f) as writer:
if not isinstance(tweets, list):
writer.write(tweets)
else:
writer.write_all(tweets)
self.f.close()
def test_writer_flags():
fp = io.BytesIO()
with jsonlines.Writer(fp, compact=True, sort_keys=True) as writer:
writer.write(collections.OrderedDict([
('b', 2),
('a', 1),
]))
assert fp.getvalue() == b'{"a":1,"b":2}\n'
def send_event(topic, payload_dict={}, show_event=True):
if show_event:
print(topic, end=" ")
with jsonlines.Writer(events_out_file) as out:
out.write(payload_dict)
events_out_file.flush()
def _save_articles_to_jsonl(results: t.Iterator[dict],
jsonl_out: pathlib.Path) -> None:
"""
Writes the relevant data to disk
"""
with open(jsonl_out, 'w', encoding='utf-8') as fp:
with jl.Writer(fp, compact=True, sort_keys=True) as writer:
for item in results:
writer.write(item)
def print_json(value: typing.Any, out_file=sys.stdout) -> None:
"""Print a single line of JSON to stdout."""
import jsonlines
with jsonlines.Writer(out_file) as out:
# pylint: disable=E1101
out.write(value)
out_file.flush()
def merge_sorted_jsonl_files(input_file_path_1: str, input_file_path_2: str,
result_file_path: str) -> None:
with open(input_file_path_1, "r") as data_read_1, open(
input_file_path_2,
"r") as data_read_2, open(result_file_path, "w") as data_write:
pairs = list(create_pairs(data_read_1, data_read_2))
writer = jsonlines.Writer(data_write)
while pairs:
write_next_min_item(pairs, writer)
def fix_ngram():
with jsonlines.open(file) as reader:
for obj in reader:
with jsonlines.Writer(
open(
os.path.join(results_dir,
"blimp_ngram_simplelm_peephole.jsonl"),
"w")) as writer:
writer.write_all(obj)
def main():
reader = csv.DictReader(sys.stdin)
writer = jsonlines.Writer(sys.stdout.buffer)
try:
writer.write_all(reader)
except BrokenPipeError:
pass
return 0
def __init__(self, file_name: str):
"""Create a file object"""
self.file_name = file_name
self._clear()
self.writer = jsonlines.Writer(fp=open(file_name,
mode="a",
encoding="utf-8"),
dumps=NumpyEncoder().encode,
flush=False)
def generate_paradigm(self,
number_to_generate=1000,
rel_output_path=None,
absolute_path=None):
if rel_output_path is not None:
project_root = "/".join(
os.path.join(os.path.dirname(
os.path.abspath(__file__))).split("/")[:-1])
output = open(os.path.join(project_root, rel_output_path), "w")
elif absolute_path is not None:
output = open(absolute_path, "w")
else:
raise Exception("You need to give an output path")
past_sentences = set()
generated_data = []
pairID = 0
error_counter = 0
constant_data = self.make_metadata_dict()
print("Generating data for " + constant_data["UID"])
self.make_logger(constant_data)
output_writer = jsonlines.Writer(output, flush=True)
while len(past_sentences) < number_to_generate:
try:
new_data, track_sentence = self.sample()
if track_sentence not in past_sentences:
past_sentences.add(track_sentence)
for field in self.data_fields:
if field in new_data:
new_data[field] = string_beautify(new_data[field])
new_data.update(constant_data)
new_data["pairID"] = str(pairID)
pairID += 1
if pairID % 100 == 0:
print("%d sentences generated" % pairID)
output_writer.write(new_data)
except Exception as e:
self.log_exception(e)
print(self.get_stack_trace(e))
error_counter += 1
if error_counter > number_to_generate // 5:
pass
# raise Exception("Over 20\% of samples result in errors. You should fix this.")
jsonlines.Writer(output).write_all(generated_data)