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testing_utils.py
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testing_utils.py
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import inspect
import logging
import os
import re
import shutil
import sys
import tempfile
import unittest
from distutils.util import strtobool
from io import StringIO
from pathlib import Path
from .file_utils import (
_datasets_available,
_faiss_available,
_flax_available,
_sentencepiece_available,
_tf_available,
_tokenizers_available,
_torch_available,
_torch_tpu_available,
)
from .integrations import _has_optuna, _has_ray
SMALL_MODEL_IDENTIFIER = "julien-c/bert-xsmall-dummy"
DUMMY_UNKWOWN_IDENTIFIER = "julien-c/dummy-unknown"
DUMMY_DIFF_TOKENIZER_IDENTIFIER = "julien-c/dummy-diff-tokenizer"
# Used to test Auto{Config, Model, Tokenizer} model_type detection.
def parse_flag_from_env(key, default=False):
try:
value = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_value = default
else:
# KEY is set, convert it to True or False.
try:
_value = strtobool(value)
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError("If set, {} must be yes or no.".format(key))
return _value
def parse_int_from_env(key, default=None):
try:
value = os.environ[key]
except KeyError:
_value = default
else:
try:
_value = int(value)
except ValueError:
raise ValueError("If set, {} must be a int.".format(key))
return _value
_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
_run_custom_tokenizers = parse_flag_from_env("RUN_CUSTOM_TOKENIZERS", default=False)
_tf_gpu_memory_limit = parse_int_from_env("TF_GPU_MEMORY_LIMIT", default=None)
def slow(test_case):
"""
Decorator marking a test as slow.
Slow tests are skipped by default. Set the RUN_SLOW environment variable
to a truthy value to run them.
"""
if not _run_slow_tests:
return unittest.skip("test is slow")(test_case)
else:
return test_case
def custom_tokenizers(test_case):
"""
Decorator marking a test for a custom tokenizer.
Custom tokenizers require additional dependencies, and are skipped
by default. Set the RUN_CUSTOM_TOKENIZERS environment variable
to a truthy value to run them.
"""
if not _run_custom_tokenizers:
return unittest.skip("test of custom tokenizers")(test_case)
else:
return test_case
def require_torch(test_case):
"""
Decorator marking a test that requires PyTorch.
These tests are skipped when PyTorch isn't installed.
"""
if not _torch_available:
return unittest.skip("test requires PyTorch")(test_case)
else:
return test_case
def require_tf(test_case):
"""
Decorator marking a test that requires TensorFlow.
These tests are skipped when TensorFlow isn't installed.
"""
if not _tf_available:
return unittest.skip("test requires TensorFlow")(test_case)
else:
return test_case
def require_flax(test_case):
"""
Decorator marking a test that requires JAX & Flax
These tests are skipped when one / both are not installed
"""
if not _flax_available:
test_case = unittest.skip("test requires JAX & Flax")(test_case)
return test_case
def require_sentencepiece(test_case):
"""
Decorator marking a test that requires SentencePiece.
These tests are skipped when SentencePiece isn't installed.
"""
if not _sentencepiece_available:
return unittest.skip("test requires SentencePiece")(test_case)
else:
return test_case
def require_tokenizers(test_case):
"""
Decorator marking a test that requires 馃 Tokenizers.
These tests are skipped when 馃 Tokenizers isn't installed.
"""
if not _tokenizers_available:
return unittest.skip("test requires tokenizers")(test_case)
else:
return test_case
def require_torch_multigpu(test_case):
"""
Decorator marking a test that requires a multi-GPU setup (in PyTorch).
These tests are skipped on a machine without multiple GPUs.
To run *only* the multigpu tests, assuming all test names contain multigpu:
$ pytest -sv ./tests -k "multigpu"
"""
if not _torch_available:
return unittest.skip("test requires PyTorch")(test_case)
import torch
if torch.cuda.device_count() < 2:
return unittest.skip("test requires multiple GPUs")(test_case)
else:
return test_case
def require_torch_non_multigpu(test_case):
"""
Decorator marking a test that requires 0 or 1 GPU setup (in PyTorch).
"""
if not _torch_available:
return unittest.skip("test requires PyTorch")(test_case)
import torch
if torch.cuda.device_count() > 1:
return unittest.skip("test requires 0 or 1 GPU")(test_case)
else:
return test_case
def require_torch_tpu(test_case):
"""
Decorator marking a test that requires a TPU (in PyTorch).
"""
if not _torch_tpu_available:
return unittest.skip("test requires PyTorch TPU")
else:
return test_case
if _torch_available:
# Set env var CUDA_VISIBLE_DEVICES="" to force cpu-mode
import torch
torch_device = "cuda" if torch.cuda.is_available() else "cpu"
else:
torch_device = None
def require_torch_gpu(test_case):
"""Decorator marking a test that requires CUDA and PyTorch. """
if torch_device != "cuda":
return unittest.skip("test requires CUDA")(test_case)
else:
return test_case
def require_datasets(test_case):
"""Decorator marking a test that requires datasets."""
if not _datasets_available:
return unittest.skip("test requires `datasets`")(test_case)
else:
return test_case
def require_faiss(test_case):
"""Decorator marking a test that requires faiss."""
if not _faiss_available:
return unittest.skip("test requires `faiss`")(test_case)
else:
return test_case
def require_optuna(test_case):
"""
Decorator marking a test that requires optuna.
These tests are skipped when optuna isn't installed.
"""
if not _has_optuna:
return unittest.skip("test requires optuna")(test_case)
else:
return test_case
def require_ray(test_case):
"""
Decorator marking a test that requires Ray/tune.
These tests are skipped when Ray/tune isn't installed.
"""
if not _has_ray:
return unittest.skip("test requires Ray/tune")(test_case)
else:
return test_case
def get_tests_dir(append_path=None):
"""
Args:
append_path: optional path to append to the tests dir path
Return:
The full path to the `tests` dir, so that the tests can be invoked from anywhere.
Optionally `append_path` is joined after the `tests` dir the former is provided.
"""
# this function caller's __file__
caller__file__ = inspect.stack()[1][1]
tests_dir = os.path.abspath(os.path.dirname(caller__file__))
if append_path:
return os.path.join(tests_dir, append_path)
else:
return tests_dir
#
# Helper functions for dealing with testing text outputs
# The original code came from:
# https://github.com/fastai/fastai/blob/master/tests/utils/text.py
# When any function contains print() calls that get overwritten, like progress bars,
# a special care needs to be applied, since under pytest -s captured output (capsys
# or contextlib.redirect_stdout) contains any temporary printed strings, followed by
# \r's. This helper function ensures that the buffer will contain the same output
# with and without -s in pytest, by turning:
# foo bar\r tar mar\r final message
# into:
# final message
# it can handle a single string or a multiline buffer
def apply_print_resets(buf):
return re.sub(r"^.*\r", "", buf, 0, re.M)
def assert_screenout(out, what):
out_pr = apply_print_resets(out).lower()
match_str = out_pr.find(what.lower())
assert match_str != -1, f"expecting to find {what} in output: f{out_pr}"
class CaptureStd:
"""Context manager to capture:
stdout, clean it up and make it available via obj.out
stderr, and make it available via obj.err
init arguments:
- out - capture stdout: True/False, default True
- err - capture stdout: True/False, default True
Examples:
with CaptureStdout() as cs:
print("Secret message")
print(f"captured: {cs.out}")
import sys
with CaptureStderr() as cs:
print("Warning: ", file=sys.stderr)
print(f"captured: {cs.err}")
# to capture just one of the streams, but not the other
with CaptureStd(err=False) as cs:
print("Secret message")
print(f"captured: {cs.out}")
# but best use the stream-specific subclasses
"""
def __init__(self, out=True, err=True):
if out:
self.out_buf = StringIO()
self.out = "error: CaptureStd context is unfinished yet, called too early"
else:
self.out_buf = None
self.out = "not capturing stdout"
if err:
self.err_buf = StringIO()
self.err = "error: CaptureStd context is unfinished yet, called too early"
else:
self.err_buf = None
self.err = "not capturing stderr"
def __enter__(self):
if self.out_buf:
self.out_old = sys.stdout
sys.stdout = self.out_buf
if self.err_buf:
self.err_old = sys.stderr
sys.stderr = self.err_buf
return self
def __exit__(self, *exc):
if self.out_buf:
sys.stdout = self.out_old
self.out = apply_print_resets(self.out_buf.getvalue())
if self.err_buf:
sys.stderr = self.err_old
self.err = self.err_buf.getvalue()
def __repr__(self):
msg = ""
if self.out_buf:
msg += f"stdout: {self.out}\n"
if self.err_buf:
msg += f"stderr: {self.err}\n"
return msg
# in tests it's the best to capture only the stream that's wanted, otherwise
# it's easy to miss things, so unless you need to capture both streams, use the
# subclasses below (less typing). Or alternatively, configure `CaptureStd` to
# disable the stream you don't need to test.
class CaptureStdout(CaptureStd):
""" Same as CaptureStd but captures only stdout """
def __init__(self):
super().__init__(err=False)
class CaptureStderr(CaptureStd):
""" Same as CaptureStd but captures only stderr """
def __init__(self):
super().__init__(out=False)
class CaptureLogger:
"""Context manager to capture `logging` streams
Args:
- logger: 'logging` logger object
Results:
The captured output is available via `self.out`
Example:
>>> from transformers import logging
>>> from transformers.testing_utils import CaptureLogger
>>> msg = "Testing 1, 2, 3"
>>> logging.set_verbosity_info()
>>> logger = logging.get_logger("transformers.tokenization_bart")
>>> with CaptureLogger(logger) as cl:
... logger.info(msg)
>>> assert cl.out, msg+"\n"
"""
def __init__(self, logger):
self.logger = logger
self.io = StringIO()
self.sh = logging.StreamHandler(self.io)
self.out = ""
def __enter__(self):
self.logger.addHandler(self.sh)
return self
def __exit__(self, *exc):
self.logger.removeHandler(self.sh)
self.out = self.io.getvalue()
def __repr__(self):
return f"captured: {self.out}\n"
class TestCasePlus(unittest.TestCase):
"""This class extends `unittest.TestCase` with additional features.
Feature 1: Flexible auto-removable temp dirs which are guaranteed to get
removed at the end of test.
In all the following scenarios the temp dir will be auto-removed at the end
of test, unless `after=False`.
# 1. create a unique temp dir, `tmp_dir` will contain the path to the created temp dir
def test_whatever(self):
tmp_dir = self.get_auto_remove_tmp_dir()
# 2. create a temp dir of my choice and delete it at the end - useful for debug when you want to
# monitor a specific directory
def test_whatever(self):
tmp_dir = self.get_auto_remove_tmp_dir(tmp_dir="./tmp/run/test")
# 3. create a temp dir of my choice and do not delete it at the end - useful for when you want
# to look at the temp results
def test_whatever(self):
tmp_dir = self.get_auto_remove_tmp_dir(tmp_dir="./tmp/run/test", after=False)
# 4. create a temp dir of my choice and ensure to delete it right away - useful for when you
# disabled deletion in the previous test run and want to make sure the that tmp dir is empty
# before the new test is run
def test_whatever(self):
tmp_dir = self.get_auto_remove_tmp_dir(tmp_dir="./tmp/run/test", before=True)
Note 1: In order to run the equivalent of `rm -r` safely, only subdirs of the
project repository checkout are allowed if an explicit `tmp_dir` is used, so
that by mistake no `/tmp` or similar important part of the filesystem will
get nuked. i.e. please always pass paths that start with `./`
Note 2: Each test can register multiple temp dirs and they all will get
auto-removed, unless requested otherwise.
"""
def setUp(self):
self.teardown_tmp_dirs = []
def get_auto_remove_tmp_dir(self, tmp_dir=None, after=True, before=False):
"""
Args:
tmp_dir (:obj:`string`, `optional`):
use this path, if None a unique path will be assigned
before (:obj:`bool`, `optional`, defaults to :obj:`False`):
if `True` and tmp dir already exists make sure to empty it right away
after (:obj:`bool`, `optional`, defaults to :obj:`True`):
delete the tmp dir at the end of the test
Returns:
tmp_dir(:obj:`string`):
either the same value as passed via `tmp_dir` or the path to the auto-created tmp dir
"""
if tmp_dir is not None:
# using provided path
path = Path(tmp_dir).resolve()
# to avoid nuking parts of the filesystem, only relative paths are allowed
if not tmp_dir.startswith("./"):
raise ValueError(
f"`tmp_dir` can only be a relative path, i.e. `./some/path`, but received `{tmp_dir}`"
)
# ensure the dir is empty to start with
if before is True and path.exists():
shutil.rmtree(tmp_dir, ignore_errors=True)
path.mkdir(parents=True, exist_ok=True)
else:
# using unique tmp dir (always empty, regardless of `before`)
tmp_dir = tempfile.mkdtemp()
if after is True:
# register for deletion
self.teardown_tmp_dirs.append(tmp_dir)
return tmp_dir
def tearDown(self):
# remove registered temp dirs
for path in self.teardown_tmp_dirs:
shutil.rmtree(path, ignore_errors=True)
self.teardown_tmp_dirs = []
def mockenv(**kwargs):
"""this is a convenience wrapper, that allows this:
@mockenv(RUN_SLOW=True, USE_TF=False)
def test_something():
run_slow = os.getenv("RUN_SLOW", False)
use_tf = os.getenv("USE_TF", False)
"""
return unittest.mock.patch.dict(os.environ, kwargs)