[Test refactor 1/5] Per-folder tests reorganization (#15725)

* Per-folder tests reorganization

Co-authored-by: sgugger <sylvain.gugger@gmail.com>
Co-authored-by: Stas Bekman <stas@stason.org>

tests/bort/test_modeling_bort.py

@@ -0,0 +1,51 @@
+# coding=utf-8
+# 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 is_torch_available
+from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import AutoModel
+
+
+@require_torch
+@require_sentencepiece
+@require_tokenizers
+class BortIntegrationTest(unittest.TestCase):
+    @slow
+    def test_output_embeds_base_model(self):
+        model = AutoModel.from_pretrained("amazon/bort")
+        model.to(torch_device)
+
+        input_ids = torch.tensor(
+            [[0, 18077, 4082, 7804, 8606, 6195, 2457, 3321, 11, 10489, 16, 269, 2579, 328, 2]],
+            device=torch_device,
+            dtype=torch.long,
+        )  # Schloß Nymphenburg in Munich is really nice!
+        output = model(input_ids)["last_hidden_state"]
+        expected_shape = torch.Size((1, 15, 1024))
+        self.assertEqual(output.shape, expected_shape)
+        # compare the actual values for a slice.
+        expected_slice = torch.tensor(
+            [[[-0.0349, 0.0436, -1.8654], [-0.6964, 0.0835, -1.7393], [-0.9819, 0.2956, -0.2868]]],
+            device=torch_device,
+            dtype=torch.float,
+        )
+        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))

tests/bort/test_modeling_tf_bort.py

@@ -0,0 +1,51 @@
+# coding=utf-8
+# 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 is_tf_available
+from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
+
+
+if is_tf_available():
+    import numpy as np
+    import tensorflow as tf
+
+    from transformers import TFAutoModel
+
+
+@require_tf
+@require_sentencepiece
+@require_tokenizers
+class TFBortIntegrationTest(unittest.TestCase):
+    @slow
+    def test_output_embeds_base_model(self):
+        model = TFAutoModel.from_pretrained("amazon/bort")
+
+        input_ids = tf.convert_to_tensor(
+            [[0, 18077, 4082, 7804, 8606, 6195, 2457, 3321, 11, 10489, 16, 269, 2579, 328, 2]],
+            dtype=tf.int32,
+        )  # Schloß Nymphenburg in Munich is really nice!
+
+        output = model(input_ids)["last_hidden_state"]
+        expected_shape = tf.TensorShape((1, 15, 1024))
+        self.assertEqual(output.shape, expected_shape)
+        # compare the actual values for a slice.
+        expected_slice = tf.convert_to_tensor(
+            [[[-0.0349, 0.0436, -1.8654], [-0.6964, 0.0835, -1.7393], [-0.9819, 0.2956, -0.2868]]],
+            dtype=tf.float32,
+        )
+
+        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))