def __init__(self, input_bytes):
"""FuzzingHelper initializer.
Args:
input_bytes: Input randomized bytes used to create a FuzzedDataProvider.
"""
self.fdp = atheris.FuzzedDataProvider(input_bytes)
def TestOneInput(input_bytes):
fdp = atheris.FuzzedDataProvider(input_bytes)
original = fdp.ConsumeUnicode(sys.maxsize)
try:
ujson_data = ujson.loads(original)
json_data = json.loads(original)
except Exception as e:
# It would be interesting to enforce that if one of the libraries throws an
# exception, the other does too. However, uJSON accepts many invalid inputs
# that are uninteresting, such as "00". So, that is not done.
return
# Uncomment these lines to ignore the errors described in the docstring of
# this file.
# json_data = ClearAllIntegers(json_data)
# ujson_data = ClearAllIntegers(ujson_data)
json_dumped = json.dumps(json_data)
ujson_dumped = json.dumps(ujson_data)
if json_dumped != ujson_dumped:
raise RuntimeError(
"Decoding/encoding disagreement!\nInput: %s\nJSON data: %s\nuJSON data: %s\nJSON-dumped: %s\nuJSON-dumped: %s\n"
% (original, json_data, ujson_data, json_dumped, ujson_dumped))
def TestOneInput(input_bytes):
fdp = atheris.FuzzedDataProvider(input_bytes)
original = fdp.ConsumeUnicode(sys.maxsize)
try:
ujson_data = ujson.loads(original)
except ValueError:
return
# We make sure there's no error in encoding, but we don't actually compare
# (encoded == original) because it's not entirely preserving. For example,
# it does not preserve whitespace.
encoded = ujson.dumps(ujson_data)
del encoded
def TestOneInput(input_bytes):
"""Test randomized integer fuzzing input for tf.raw_ops.RaggedCountSparseOutput."""
fdp = atheris.FuzzedDataProvider(input_bytes)
random_split_length = fdp.ConsumeIntInRange(0, 500)
random_length = fdp.ConsumeIntInRange(501, 1000)
splits = fdp.ConsumeIntListInRange(random_split_length, 1, 100000)
# First value of splits has to be 0.
splits.insert(0, 0)
# Last value of splits has to be length of the values/weights.
splits.append(random_length)
values = fdp.ConsumeIntListInRange(random_length, 0, 100000)
weights = fdp.ConsumeIntListInRange(random_length, 0, 100000)
_, _, _, = tf.raw_ops.RaggedCountSparseOutput(splits=splits,
values=values,
weights=weights,
binary_output=False)
