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# 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 unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class FlaxAutoModelTest(unittest.TestCase):
@slow
def test_bert_from_pretrained(self):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(model_name):
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = FlaxAutoModel.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, FlaxBertModel)
@slow
def test_roberta_from_pretrained(self):
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(model_name):
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = FlaxAutoModel.from_pretrained(model_name)
self.assertIsNotNone(model)
self.assertIsInstance(model, FlaxRobertaModel)
@slow
def test_bert_jax_jit(self):
for model_name in ["bert-base-cased", "bert-large-uncased"]:
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = FlaxBertModel.from_pretrained(model_name)
tokens = tokenizer("Do you support jax jitted function?", return_tensors=TensorType.JAX)
@jax.jit
def eval(**kwargs):
return model(**kwargs)
eval(**tokens).block_until_ready()
@slow
def test_roberta_jax_jit(self):
for model_name in ["roberta-base", "roberta-large"]:
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = FlaxRobertaModel.from_pretrained(model_name)
tokens = tokenizer("Do you support jax jitted function?", return_tensors=TensorType.JAX)
@jax.jit
def eval(**kwargs):
return model(**kwargs)
eval(**tokens).block_until_ready()
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