Take advantage of the cache when running tests.

Caching models across test cases and across runs of the test suite makes
slow tests somewhat more bearable.

Use gettempdir() instead of /tmp in tests. This makes it easier to
change the location of the cache with semi-standard TMPDIR/TEMP/TMP
environment variables.

Fix #2222.

templates/adding_a_new_model/tests/modeling_tf_xxx_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import XxxConfig, is_tf_available
 
@@ -245,10 +244,8 @@ class TFXxxModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in ['xxx-base-uncased']:
-            model = TFXxxModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFXxxModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

templates/adding_a_new_model/tests/modeling_xxx_test.py

@@ -17,13 +17,12 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (XxxConfig, XxxModel, XxxForMaskedLM,
@@ -249,10 +248,8 @@ class XxxModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(XXX_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = XxxModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = XxxModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_albert_test.py

@@ -17,13 +17,12 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (AlbertConfig, AlbertModel, AlbertForMaskedLM,
@@ -230,10 +229,8 @@ class AlbertModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = AlbertModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = AlbertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_bert_test.py

@@ -17,13 +17,12 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor, floats_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (BertConfig, BertModel, BertForMaskedLM,
@@ -360,10 +359,8 @@ class BertModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = BertModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = BertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_common_test.py

@@ -30,7 +30,7 @@ import logging
 
 from transformers import is_torch_available
 
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     import torch
@@ -753,10 +753,8 @@ class CommonTestCases:
                 [[], []])
 
         def create_and_check_model_from_pretrained(self):
-            cache_dir = "/tmp/transformers_test/"
             for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]:
-                model = self.base_model_class.from_pretrained(model_name, cache_dir=cache_dir)
-                shutil.rmtree(cache_dir)
+                model = self.base_model_class.from_pretrained(model_name, cache_dir=CACHE_DIR)
                 self.parent.assertIsNotNone(model)
 
         def prepare_config_and_inputs_for_common(self):

transformers/tests/modeling_ctrl_test.py

@@ -16,7 +16,6 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import pdb
 
 from transformers import is_torch_available
@@ -27,7 +26,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -205,10 +204,8 @@ class CTRLModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = CTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = CTRLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_distilbert_test.py

@@ -27,7 +27,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -235,10 +235,8 @@ class DistilBertModelTest(CommonTestCases.CommonModelTester):
 
     # @slow
     # def test_model_from_pretrained(self):
-    #     cache_dir = "/tmp/transformers_test/"
     #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-    #         model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
-    #         shutil.rmtree(cache_dir)
+    #         model = DistilBertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
     #         self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_gpt2_test.py

@@ -17,7 +17,6 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
@@ -27,7 +26,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -239,10 +238,8 @@ class GPT2ModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = GPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = GPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_openai_test.py

@@ -17,7 +17,6 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
@@ -27,7 +26,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -207,10 +206,8 @@ class OpenAIGPTModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = OpenAIGPTModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = OpenAIGPTModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_roberta_test.py

@@ -17,7 +17,6 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
@@ -29,7 +28,7 @@ if is_torch_available():
 
 from .modeling_common_test import (CommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 
 @require_torch
@@ -199,10 +198,8 @@ class RobertaModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = RobertaModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = RobertaModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_t5_test.py

@@ -17,13 +17,12 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from transformers import is_torch_available
 
 from .modeling_common_test import (CommonTestCases, ids_tensor, floats_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_torch, slow, torch_device
+from .utils import CACHE_DIR, require_torch, slow, torch_device
 
 if is_torch_available():
     from transformers import (T5Config, T5Model, T5WithLMHeadModel)
@@ -175,10 +174,8 @@ class T5ModelTest(CommonTestCases.CommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(T5_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = T5Model.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = T5Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_albert_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import AlbertConfig, is_tf_available
 
@@ -217,12 +216,9 @@ class TFAlbertModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         # for model_name in list(TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['albert-base-uncased']:
-            model = TFAlbertModel.from_pretrained(
-                model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFAlbertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_tf_auto_test.py

@@ -46,11 +46,11 @@ class TFAutoModelTest(unittest.TestCase):
         logging.basicConfig(level=logging.INFO)
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            config = AutoConfig.from_pretrained(model_name, force_download=True)
+            config = AutoConfig.from_pretrained(model_name)
             self.assertIsNotNone(config)
             self.assertIsInstance(config, BertConfig)
 
-            model = TFAutoModel.from_pretrained(model_name, force_download=True)
+            model = TFAutoModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
             self.assertIsInstance(model, TFBertModel)
 
@@ -59,11 +59,11 @@ class TFAutoModelTest(unittest.TestCase):
         logging.basicConfig(level=logging.INFO)
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            config = AutoConfig.from_pretrained(model_name, force_download=True)
+            config = AutoConfig.from_pretrained(model_name)
             self.assertIsNotNone(config)
             self.assertIsInstance(config, BertConfig)
 
-            model = TFAutoModelWithLMHead.from_pretrained(model_name, force_download=True)
+            model = TFAutoModelWithLMHead.from_pretrained(model_name)
             self.assertIsNotNone(model)
             self.assertIsInstance(model, TFBertForMaskedLM)
 
@@ -72,11 +72,11 @@ class TFAutoModelTest(unittest.TestCase):
         logging.basicConfig(level=logging.INFO)
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            config = AutoConfig.from_pretrained(model_name, force_download=True)
+            config = AutoConfig.from_pretrained(model_name)
             self.assertIsNotNone(config)
             self.assertIsInstance(config, BertConfig)
 
-            model = TFAutoModelForSequenceClassification.from_pretrained(model_name, force_download=True)
+            model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
             self.assertIsNotNone(model)
             self.assertIsInstance(model, TFBertForSequenceClassification)
 
@@ -85,17 +85,17 @@ class TFAutoModelTest(unittest.TestCase):
         logging.basicConfig(level=logging.INFO)
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            config = AutoConfig.from_pretrained(model_name, force_download=True)
+            config = AutoConfig.from_pretrained(model_name)
             self.assertIsNotNone(config)
             self.assertIsInstance(config, BertConfig)
 
-            model = TFAutoModelForQuestionAnswering.from_pretrained(model_name, force_download=True)
+            model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
             self.assertIsNotNone(model)
             self.assertIsInstance(model, TFBertForQuestionAnswering)
 
     def test_from_pretrained_identifier(self):
         logging.basicConfig(level=logging.INFO)
-        model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER, force_download=True)
+        model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
         self.assertIsInstance(model, TFBertForMaskedLM)
 
 

transformers/tests/modeling_tf_bert_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import BertConfig, is_tf_available
 
@@ -310,11 +309,9 @@ class TFBertModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         # for model_name in list(TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
         for model_name in ['bert-base-uncased']:
-            model = TFBertModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFBertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_ctrl_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import CTRLConfig, is_tf_available
 
@@ -189,10 +188,8 @@ class TFCTRLModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = TFCTRLModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFCTRLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_distilbert_test.py

@@ -20,7 +20,7 @@ import unittest
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import DistilBertConfig, is_tf_available
 
@@ -211,10 +211,8 @@ class TFDistilBertModelTest(TFCommonTestCases.TFCommonModelTester):
 
     # @slow
     # def test_model_from_pretrained(self):
-    #     cache_dir = "/tmp/transformers_test/"
     #     for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-    #         model = DistilBertModel.from_pretrained(model_name, cache_dir=cache_dir)
-    #         shutil.rmtree(cache_dir)
+    #         model = DistilBertModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
     #         self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_gpt2_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import GPT2Config, is_tf_available
 
@@ -220,10 +219,8 @@ class TFGPT2ModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_GPT2_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = TFGPT2Model.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFGPT2Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_openai_gpt_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import OpenAIGPTConfig, is_tf_available
 
@@ -219,10 +218,8 @@ class TFOpenAIGPTModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = TFOpenAIGPTModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFOpenAIGPTModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":

transformers/tests/modeling_tf_roberta_test.py

@@ -17,11 +17,10 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import RobertaConfig, is_tf_available
 
@@ -192,10 +191,8 @@ class TFRobertaModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in list(TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
-            model = TFRobertaModel.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFRobertaModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 

transformers/tests/modeling_tf_t5_test.py

@@ -17,12 +17,11 @@ from __future__ import division
 from __future__ import print_function
 
 import unittest
-import shutil
 import sys
 
 from .modeling_tf_common_test import (TFCommonTestCases, ids_tensor)
 from .configuration_common_test import ConfigTester
-from .utils import require_tf, slow
+from .utils import CACHE_DIR, require_tf, slow
 
 from transformers import T5Config, is_tf_available
 
@@ -162,10 +161,8 @@ class TFT5ModelTest(TFCommonTestCases.TFCommonModelTester):
 
     @slow
     def test_model_from_pretrained(self):
-        cache_dir = "/tmp/transformers_test/"
         for model_name in ['t5-small']:
-            model = TFT5Model.from_pretrained(model_name, cache_dir=cache_dir)
-            shutil.rmtree(cache_dir)
+            model = TFT5Model.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)
 
 if __name__ == "__main__":