(refactor) get out train.py from train folder

@@ -2,6 +2,7 @@ whatthepatch
 gitpython
 matorage
 transformers
+packaging
 
 psutil
 sacrebleu

+# Copyright 2020-present Tae Hwan Jung
+# 
+# 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 os
+import argparse
+import pytorch_lightning as pl
+from train.finetune import main, SummarizationModule
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
+
+    args = parser.parse_args()
+
+    main(args)
\ No newline at end of file

@@ -12,7 +12,7 @@ import pytorch_lightning as pl
 import torch
 from torch.utils.data import DataLoader
 
-from lightning_base import BaseTransformer, add_generic_args, generic_train
+from train.lightning_base import BaseTransformer, add_generic_args, generic_train
 from transformers import MBartTokenizer, T5ForConditionalGeneration
 from transformers.modeling_bart import shift_tokens_right
 
@@ -260,16 +260,16 @@ class SummarizationModule(BaseTransformer):
     def get_dataset(self, type_path) -> Seq2SeqDataset:
         max_target_length = self.target_lens[type_path]
         data_config = DataConfig(
-            endpoint=args.endpoint,
+            endpoint=self.hparams.endpoint,
             access_key=os.environ["access_key"],
             secret_key=os.environ["secret_key"],
-            region=args.region,
+            region=self.hparams.region,
             dataset_name="commit-autosuggestions",
             additional={
                 "mode": ("training" if type_path == "train" else "evaluation"),
                 "max_source_length": self.hparams.max_source_length,
                 "max_target_length": max_target_length,
-                "url": args.url,
+                "url": self.hparams.url,
             },
             attributes=[
                 ("input_ids", "int32", (self.hparams.max_source_length,)),
@@ -461,14 +461,4 @@ def main(args, model=None) -> SummarizationModule:
 
     # test() without a model tests using the best checkpoint automatically
     trainer.test()
-    return model
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
-    parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())
-
-    args = parser.parse_args()
-
-    main(args)
+    return model
\ No newline at end of file

@@ -21,7 +21,7 @@ from transformers import (
     PretrainedConfig,
     PreTrainedTokenizer,
 )
-from modeling_bart import BartForConditionalGeneration
+from train.modeling_bart import BartForConditionalGeneration
 
 from transformers.optimization import (
     Adafactor,

@@ -41,7 +41,7 @@ from transformers.modeling_outputs import (
     Seq2SeqQuestionAnsweringModelOutput,
     Seq2SeqSequenceClassifierOutput,
 )
-from modeling_utils import PreTrainedModel
+from train.modeling_utils import PreTrainedModel
 import logging
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name

@@ -39,7 +39,7 @@ from transformers.file_utils import (
     is_torch_tpu_available,
     replace_return_docstrings,
 )
-from generation_utils import GenerationMixin
+from train.generation_utils import GenerationMixin
 import logging
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name