Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
File size: 2,493 Bytes
75466df |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
import os
import tempfile
import unittest
from distutils.util import strtobool
from transformers.file_utils import _tf_available, _torch_available
CACHE_DIR = os.path.join(tempfile.gettempdir(), "transformers_test")
SMALL_MODEL_IDENTIFIER = "julien-c/bert-xsmall-dummy"
DUMMY_UNKWOWN_IDENTIFIER = "julien-c/dummy-unknown"
# 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
_run_slow_tests = parse_flag_from_env("RUN_SLOW", default=False)
_run_custom_tokenizers = parse_flag_from_env("RUN_CUSTOM_TOKENIZERS", default=False)
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:
test_case = unittest.skip("test is slow")(test_case)
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:
test_case = unittest.skip("test of custom tokenizers")(test_case)
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:
test_case = unittest.skip("test requires PyTorch")(test_case)
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:
test_case = unittest.skip("test requires TensorFlow")(test_case)
return test_case
if _torch_available:
# Set the USE_CUDA environment variable to select a GPU.
torch_device = "cuda" if parse_flag_from_env("USE_CUDA") else "cpu"
else:
torch_device = None
|