test_file_utils.py

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


MODEL_ID = DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

REVISION_ID_DEFAULT = "main"
# Default branch name
REVISION_ID_ONE_SPECIFIC_COMMIT = "f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
REVISION_ID_INVALID = "aaaaaaa"
# This commit does not exist, so we should 404.

PINNED_SHA1 = "d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
PINNED_SHA256 = "4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"
# Sha-256 of pytorch_model.bin on the top of `main`, for checking purposes


# Dummy contexts to test `ContextManagers`
@contextlib.contextmanager
def context_en():
    print("Welcome!")
    yield
    print("Bye!")


@contextlib.contextmanager
def context_fr():
    print("Bonjour!")
    yield
    print("Au revoir!")


class TestImportMechanisms(unittest.TestCase):
    def test_module_spec_available(self):
        # If the spec is missing, importlib would not be able to import the module dynamically.
        assert transformers.__spec__ is not None
        assert importlib.util.find_spec("transformers") is not None


class GenericUtilTests(unittest.TestCase):
    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
    def test_context_managers_no_context(self, mock_stdout):
        with ContextManagers([]):
            print("Transformers are awesome!")
        # The print statement adds a new line at the end of the output
        self.assertEqual(mock_stdout.getvalue(), "Transformers are awesome!\n")

    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
    def test_context_managers_one_context(self, mock_stdout):
        with ContextManagers([context_en()]):
            print("Transformers are awesome!")
        # The output should be wrapped with an English welcome and goodbye
        self.assertEqual(mock_stdout.getvalue(), "Welcome!\nTransformers are awesome!\nBye!\n")

    @unittest.mock.patch("sys.stdout", new_callable=io.StringIO)
    def test_context_managers_two_context(self, mock_stdout):
        with ContextManagers([context_fr(), context_en()]):
            print("Transformers are awesome!")
        # The output should be wrapped with an English and French welcome and goodbye
        self.assertEqual(mock_stdout.getvalue(), "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n")

    def test_find_labels(self):
        if is_torch_available():
            from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

            self.assertEqual(find_labels(BertForSequenceClassification), ["labels"])
            self.assertEqual(find_labels(BertForPreTraining), ["labels", "next_sentence_label"])
            self.assertEqual(find_labels(BertForQuestionAnswering), ["start_positions", "end_positions"])

        if is_tf_available():
            from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

            self.assertEqual(find_labels(TFBertForSequenceClassification), ["labels"])
            self.assertEqual(find_labels(TFBertForPreTraining), ["labels", "next_sentence_label"])
            self.assertEqual(find_labels(TFBertForQuestionAnswering), ["start_positions", "end_positions"])

        if is_flax_available():
            # Flax models don't have labels
            from transformers import (
                FlaxBertForPreTraining,
                FlaxBertForQuestionAnswering,
                FlaxBertForSequenceClassification,
            )

            self.assertEqual(find_labels(FlaxBertForSequenceClassification), [])
            self.assertEqual(find_labels(FlaxBertForPreTraining), [])
            self.assertEqual(find_labels(FlaxBertForQuestionAnswering), [])