Solve code formatting problem for ci

modules/text/machine_translation/transformer/en-de/module.py

@@ -82,23 +82,24 @@ class MTTransformer(nn.Layer):
 
         self.max_length = max_length
         self.beam_size = beam_size
-        self.tokenizer = MTTokenizer(bpe_codes_file=bpe_codes_file,
-                                     lang_src=self.lang_config['source'],
-                                     lang_trg=self.lang_config['target'])
-        self.vocab = Vocab.load_vocabulary(filepath=vocab_file,
-                                           unk_token=self.vocab_config['unk_token'],
-                                           bos_token=self.vocab_config['bos_token'],
-                                           eos_token=self.vocab_config['eos_token'])
+        self.tokenizer = MTTokenizer(
+            bpe_codes_file=bpe_codes_file, lang_src=self.lang_config['source'], lang_trg=self.lang_config['target'])
+        self.vocab = Vocab.load_vocabulary(
+            filepath=vocab_file,
+            unk_token=self.vocab_config['unk_token'],
+            bos_token=self.vocab_config['bos_token'],
+            eos_token=self.vocab_config['eos_token'])
         self.vocab_size = (len(self.vocab) + self.vocab_config['pad_factor'] - 1) \
             // self.vocab_config['pad_factor'] * self.vocab_config['pad_factor']
-        self.transformer = InferTransformerModel(src_vocab_size=self.vocab_size,
-                                                 trg_vocab_size=self.vocab_size,
-                                                 bos_id=self.vocab_config['bos_id'],
-                                                 eos_id=self.vocab_config['eos_id'],
-                                                 max_length=self.max_length + 1,
-                                                 max_out_len=max_out_len,
-                                                 beam_size=self.beam_size,
-                                                 **self.model_config)
+        self.transformer = InferTransformerModel(
+            src_vocab_size=self.vocab_size,
+            trg_vocab_size=self.vocab_size,
+            bos_id=self.vocab_config['bos_id'],
+            eos_id=self.vocab_config['eos_id'],
+            max_length=self.max_length + 1,
+            max_out_len=max_out_len,
+            beam_size=self.beam_size,
+            **self.model_config)
 
         state_dict = paddle.load(checkpoint)
 

modules/text/machine_translation/transformer/en-de/utils.py

@@ -24,11 +24,12 @@ class MTTokenizer(object):
     def __init__(self, bpe_codes_file: str, lang_src: str = 'en', lang_trg: str = 'de', separator='@@'):
         self.moses_tokenizer = MosesTokenizer(lang=lang_src)
         self.moses_detokenizer = MosesDetokenizer(lang=lang_trg)
-        self.bpe_tokenizer = BPE(codes=codecs.open(bpe_codes_file, encoding='utf-8'),
-                                 merges=-1,
-                                 separator=separator,
-                                 vocab=None,
-                                 glossaries=None)
+        self.bpe_tokenizer = BPE(
+            codes=codecs.open(bpe_codes_file, encoding='utf-8'),
+            merges=-1,
+            separator=separator,
+            vocab=None,
+            glossaries=None)
 
     def tokenize(self, text: str):
         """

modules/text/machine_translation/transformer/zh-en/module.py

@@ -83,29 +83,31 @@ class MTTransformer(nn.Layer):
 
         self.max_length = max_length
         self.beam_size = beam_size
-        self.tokenizer = MTTokenizer(bpe_codes_file=bpe_codes_file,
-                                     lang_src=self.lang_config['source'],
-                                     lang_trg=self.lang_config['target'])
-        self.src_vocab = Vocab.load_vocabulary(filepath=src_vocab_file,
-                                               unk_token=self.vocab_config['unk_token'],
-                                               bos_token=self.vocab_config['bos_token'],
-                                               eos_token=self.vocab_config['eos_token'])
-        self.trg_vocab = Vocab.load_vocabulary(filepath=trg_vocab_file,
-                                               unk_token=self.vocab_config['unk_token'],
-                                               bos_token=self.vocab_config['bos_token'],
-                                               eos_token=self.vocab_config['eos_token'])
+        self.tokenizer = MTTokenizer(
+            bpe_codes_file=bpe_codes_file, lang_src=self.lang_config['source'], lang_trg=self.lang_config['target'])
+        self.src_vocab = Vocab.load_vocabulary(
+            filepath=src_vocab_file,
+            unk_token=self.vocab_config['unk_token'],
+            bos_token=self.vocab_config['bos_token'],
+            eos_token=self.vocab_config['eos_token'])
+        self.trg_vocab = Vocab.load_vocabulary(
+            filepath=trg_vocab_file,
+            unk_token=self.vocab_config['unk_token'],
+            bos_token=self.vocab_config['bos_token'],
+            eos_token=self.vocab_config['eos_token'])
         self.src_vocab_size = (len(self.src_vocab) + self.vocab_config['pad_factor'] - 1) \
             // self.vocab_config['pad_factor'] * self.vocab_config['pad_factor']
         self.trg_vocab_size = (len(self.trg_vocab) + self.vocab_config['pad_factor'] - 1) \
             // self.vocab_config['pad_factor'] * self.vocab_config['pad_factor']
-        self.transformer = InferTransformerModel(src_vocab_size=self.src_vocab_size,
-                                                 trg_vocab_size=self.trg_vocab_size,
-                                                 bos_id=self.vocab_config['bos_id'],
-                                                 eos_id=self.vocab_config['eos_id'],
-                                                 max_length=self.max_length + 1,
-                                                 max_out_len=max_out_len,
-                                                 beam_size=self.beam_size,
-                                                 **self.model_config)
+        self.transformer = InferTransformerModel(
+            src_vocab_size=self.src_vocab_size,
+            trg_vocab_size=self.trg_vocab_size,
+            bos_id=self.vocab_config['bos_id'],
+            eos_id=self.vocab_config['eos_id'],
+            max_length=self.max_length + 1,
+            max_out_len=max_out_len,
+            beam_size=self.beam_size,
+            **self.model_config)
 
         state_dict = paddle.load(checkpoint)
 

modules/text/machine_translation/transformer/zh-en/utils.py

@@ -28,11 +28,12 @@ from subword_nmt.apply_bpe import BPE
 class MTTokenizer(object):
     def __init__(self, bpe_codes_file: str, lang_src: str = 'zh', lang_trg: str = 'en', separator='@@'):
         self.moses_detokenizer = MosesDetokenizer(lang=lang_trg)
-        self.bpe_tokenizer = BPE(codes=codecs.open(bpe_codes_file, encoding='utf-8'),
-                                 merges=-1,
-                                 separator=separator,
-                                 vocab=None,
-                                 glossaries=None)
+        self.bpe_tokenizer = BPE(
+            codes=codecs.open(bpe_codes_file, encoding='utf-8'),
+            merges=-1,
+            separator=separator,
+            vocab=None,
+            glossaries=None)
 
     def tokenize(self, text: str):
         """