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from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def np_sum(x): # picklable for multiprocessing
return x.sum()
def add_one(i): # picklable for multiprocessing
return i + 1
@dataclass
class A:
x: int
y: str
class PyUtilsTest(TestCase):
def test_map_nested(self):
s1 = {}
s2 = []
s3 = 1
s4 = [1, 2]
s5 = {"a": 1, "b": 2}
s6 = {"a": [1, 2], "b": [3, 4]}
s7 = {"a": {"1": 1}, "b": 2}
s8 = {"a": 1, "b": 2, "c": 3, "d": 4}
expected_map_nested_s1 = {}
expected_map_nested_s2 = []
expected_map_nested_s3 = 2
expected_map_nested_s4 = [2, 3]
expected_map_nested_s5 = {"a": 2, "b": 3}
expected_map_nested_s6 = {"a": [2, 3], "b": [4, 5]}
expected_map_nested_s7 = {"a": {"1": 2}, "b": 3}
expected_map_nested_s8 = {"a": 2, "b": 3, "c": 4, "d": 5}
self.assertEqual(map_nested(add_one, s1), expected_map_nested_s1)
self.assertEqual(map_nested(add_one, s2), expected_map_nested_s2)
self.assertEqual(map_nested(add_one, s3), expected_map_nested_s3)
self.assertEqual(map_nested(add_one, s4), expected_map_nested_s4)
self.assertEqual(map_nested(add_one, s5), expected_map_nested_s5)
self.assertEqual(map_nested(add_one, s6), expected_map_nested_s6)
self.assertEqual(map_nested(add_one, s7), expected_map_nested_s7)
self.assertEqual(map_nested(add_one, s8), expected_map_nested_s8)
num_proc = 2
self.assertEqual(map_nested(add_one, s1, num_proc=num_proc), expected_map_nested_s1)
self.assertEqual(map_nested(add_one, s2, num_proc=num_proc), expected_map_nested_s2)
self.assertEqual(map_nested(add_one, s3, num_proc=num_proc), expected_map_nested_s3)
self.assertEqual(map_nested(add_one, s4, num_proc=num_proc), expected_map_nested_s4)
self.assertEqual(map_nested(add_one, s5, num_proc=num_proc), expected_map_nested_s5)
self.assertEqual(map_nested(add_one, s6, num_proc=num_proc), expected_map_nested_s6)
self.assertEqual(map_nested(add_one, s7, num_proc=num_proc), expected_map_nested_s7)
self.assertEqual(map_nested(add_one, s8, num_proc=num_proc), expected_map_nested_s8)
sn1 = {"a": np.eye(2), "b": np.zeros(3), "c": np.ones(2)}
expected_map_nested_sn1_sum = {"a": 2, "b": 0, "c": 2}
expected_map_nested_sn1_int = {
"a": np.eye(2).astype(int),
"b": np.zeros(3).astype(int),
"c": np.ones(2).astype(int),
}
self.assertEqual(map_nested(np_sum, sn1, map_numpy=False), expected_map_nested_sn1_sum)
self.assertEqual(
{k: v.tolist() for k, v in map_nested(int, sn1, map_numpy=True).items()},
{k: v.tolist() for k, v in expected_map_nested_sn1_int.items()},
)
self.assertEqual(map_nested(np_sum, sn1, map_numpy=False, num_proc=num_proc), expected_map_nested_sn1_sum)
self.assertEqual(
{k: v.tolist() for k, v in map_nested(int, sn1, map_numpy=True, num_proc=num_proc).items()},
{k: v.tolist() for k, v in expected_map_nested_sn1_int.items()},
)
with self.assertRaises(AttributeError): # can't pickle a local lambda
map_nested(lambda x: x + 1, sn1, num_proc=num_proc)
def test_zip_dict(self):
d1 = {"a": 1, "b": 2}
d2 = {"a": 3, "b": 4}
d3 = {"a": 5, "b": 6}
expected_zip_dict_result = sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))])
self.assertEqual(sorted(zip_dict(d1, d2, d3)), expected_zip_dict_result)
def test_temporary_assignment(self):
class Foo:
my_attr = "bar"
foo = Foo()
self.assertEqual(foo.my_attr, "bar")
with temporary_assignment(foo, "my_attr", "BAR"):
self.assertEqual(foo.my_attr, "BAR")
self.assertEqual(foo.my_attr, "bar")
@pytest.mark.parametrize(
"iterable_length, num_proc, expected_num_proc",
[
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
],
)
def test_map_nested_num_proc(iterable_length, num_proc, expected_num_proc):
with patch("datasets.utils.py_utils._single_map_nested") as mock_single_map_nested, patch(
"datasets.parallel.parallel.Pool"
) as mock_multiprocessing_pool:
data_struct = {f"{i}": i for i in range(iterable_length)}
_ = map_nested(lambda x: x + 10, data_struct, num_proc=num_proc, parallel_min_length=16)
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class TempSeedTest(TestCase):
@require_tf
def test_tensorflow(self):
import tensorflow as tf
from tensorflow.keras import layers
model = layers.Dense(2)
def gen_random_output():
x = tf.random.uniform((1, 3))
return model(x).numpy()
with temp_seed(42, set_tensorflow=True):
out1 = gen_random_output()
with temp_seed(42, set_tensorflow=True):
out2 = gen_random_output()
out3 = gen_random_output()
np.testing.assert_equal(out1, out2)
self.assertGreater(np.abs(out1 - out3).sum(), 0)
@require_torch
def test_torch(self):
import torch
def gen_random_output():
model = torch.nn.Linear(3, 2)
x = torch.rand(1, 3)
return model(x).detach().numpy()
with temp_seed(42, set_pytorch=True):
out1 = gen_random_output()
with temp_seed(42, set_pytorch=True):
out2 = gen_random_output()
out3 = gen_random_output()
np.testing.assert_equal(out1, out2)
self.assertGreater(np.abs(out1 - out3).sum(), 0)
def test_numpy(self):
def gen_random_output():
return np.random.rand(1, 3)
with temp_seed(42):
out1 = gen_random_output()
with temp_seed(42):
out2 = gen_random_output()
out3 = gen_random_output()
np.testing.assert_equal(out1, out2)
self.assertGreater(np.abs(out1 - out3).sum(), 0)
@pytest.mark.parametrize("input_data", [{}])
def test_nested_data_structure_data(input_data):
output_data = NestedDataStructure(input_data).data
assert output_data == input_data
@pytest.mark.parametrize(
"data, expected_output",
[
({}, []),
([], []),
("foo", ["foo"]),
(["foo", "bar"], ["foo", "bar"]),
([["foo", "bar"]], ["foo", "bar"]),
([[["foo"], ["bar"]]], ["foo", "bar"]),
([[["foo"], "bar"]], ["foo", "bar"]),
({"a": 1, "b": 2}, [1, 2]),
({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
({"a": {"1": 1}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": 2}, [1, 2]),
({"a": {"1": [1]}, "b": [2]}, [1, 2]),
],
)
def test_flatten(data, expected_output):
output = NestedDataStructure(data).flatten()
assert output == expected_output
def test_asdict():
input = A(x=1, y="foobar")
expected_output = {"x": 1, "y": "foobar"}
assert asdict(input) == expected_output
input = {"a": {"b": A(x=10, y="foo")}, "c": [A(x=20, y="bar")]}
expected_output = {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
assert asdict(input) == expected_output
with pytest.raises(TypeError):
asdict([1, A(x=10, y="foo")])
def _split_text(text: str):
return text.split()
def _2seconds_generator_of_2items_with_timing(content):
yield (time.time(), content)
time.sleep(2)
yield (time.time(), content)
def test_iflatmap_unordered():
with Pool(2) as pool:
out = list(iflatmap_unordered(pool, _split_text, kwargs_iterable=[{"text": "hello there"}] * 10))
assert out.count("hello") == 10
assert out.count("there") == 10
assert len(out) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2) as pool:
out = list(iflatmap_unordered(pool, _split_text, kwargs_iterable=[{"text": "hello there"}] * 10))
assert out.count("hello") == 10
assert out.count("there") == 10
assert len(out) == 20
# check that we get items as fast as possible
with Pool(2) as pool:
out = []
for yield_time, content in iflatmap_unordered(
pool, _2seconds_generator_of_2items_with_timing, kwargs_iterable=[{"content": "a"}, {"content": "b"}]
):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(content)
assert out.count("a") == 2
assert out.count("b") == 2
assert len(out) == 4
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