def _wiki_articles(shard_id, wikis_dir=None):
"""Generates WikipediaArticles from GCS that are part of shard shard_id."""
if not wikis_dir:
wikis_dir = WIKI_CONTENT_DIR
with tf.Graph().as_default():
dataset = tf.data.TFRecordDataset(
cc_utils.readahead(
os.path.join(wikis_dir, WIKI_CONTENT_FILE % shard_id)),
buffer_size=16 * 1000 * 1000)
def _parse_example(ex_ser):
"""Parse serialized Example containing Wikipedia article content."""
features = {
"url": tf.VarLenFeature(tf.string),
"title": tf.VarLenFeature(tf.string),
"section_titles": tf.VarLenFeature(tf.string),
"section_texts": tf.VarLenFeature(tf.string),
}
ex = tf.parse_single_example(ex_ser, features)
for k in ex.keys():
ex[k] = ex[k].values
ex["url"] = ex["url"][0]
ex["title"] = ex["title"][0]
return ex
dataset = dataset.map(_parse_example, num_parallel_calls=32)
dataset = dataset.prefetch(100)
record_it = dataset.make_one_shot_iterator().get_next()
with tf.Session() as sess:
while True:
try:
ex = sess.run(record_it)
except tf.errors.OutOfRangeError:
break
sections = [
WikipediaSection(title=text_encoder.to_unicode(title),
text=text_encoder.to_unicode(text))
for title, text in zip(ex["section_titles"], ex["section_texts"])
]
yield WikipediaArticle(
url=text_encoder.to_unicode(ex["url"]),
title=text_encoder.to_unicode(ex["title"]),
sections=sections)
def _references_content(ref_files):
"""Returns dict<str ref_url, str ref_content>."""
example_spec = {
"url": tf.FixedLenFeature([], tf.string),
"content": tf.FixedLenFeature([], tf.string),
}
data = {}
for ex in generator_utils.tfrecord_iterator(
ref_files, gzipped=True, example_spec=example_spec):
data[ex["url"]] = text_encoder.to_unicode(ex["content"])
return data
def shell_output(cmd_, **kwargs):
return text_encoder.to_unicode(sp.check_output(format_cmd(cmd_, **kwargs)))
def shell_output(cmd_, **kwargs): return text_encoder.to_unicode(sp.check_output(format_cmd(cmd_, **kwargs)))
