style: Reformat code using black

laser_encoders/__init__.py

@@ -12,4 +12,8 @@
 #
 # -------------------------------------------------------
 
-from laser_encoders.download_models import initialize_encoder, initialize_tokenizer
+from laser_encoders.download_models import (
+    LaserEncoderPipeline,
+    initialize_encoder,
+    initialize_tokenizer,
+)

laser_encoders/download_models.py

@@ -160,10 +160,8 @@ def initialize_encoder(
     if not os.path.exists(spm_vocab):
         # if there is no cvocab for the laser3 lang use laser2 cvocab
         spm_vocab = os.path.join(model_dir, "laser2.cvocab")
-
-    return SentenceEncoder(
-        model_path=model_path, spm_vocab=spm_vocab, spm_model=None
-    )
+
+    return SentenceEncoder(model_path=model_path, spm_vocab=spm_vocab, spm_model=None)
 
 
 def initialize_tokenizer(lang: str = None, model_dir: str = None, laser: str = None):
@@ -199,24 +197,32 @@ def initialize_tokenizer(lang: str = None, model_dir: str = None, laser: str = N
 
 
 class LaserEncoderPipeline:
-    def __init__(self, lang: str, model_dir: str = None, spm: bool = True, laser: str = None):
+    def __init__(
+        self, lang: str, model_dir: str = None, spm: bool = True, laser: str = None
+    ):
+        self.tokenizer = initialize_tokenizer(
+            lang=lang, model_dir=model_dir, laser=laser
+        )
+        self.encoder = initialize_encoder(
+            lang=lang, model_dir=model_dir, spm=spm, laser=laser
+        )
 
-        self.tokenizer = initialize_tokenizer(lang=lang, model_dir=model_dir, laser=laser)
-        self.encoder = initialize_encoder(lang=lang, model_dir=model_dir, spm=spm,laser=laser)
-
     def encode_sentences(self, sentences: list) -> list:
         """
         Tokenizes and encodes a list of sentences.
-        
+
         Args:
         - sentences (list of str): List of sentences to tokenize and encode.
 
         Returns:
         - List of embeddings for each sentence.
         """
-        tokenized_sentences = [self.tokenizer.tokenize(sentence) for sentence in sentences]
+        tokenized_sentences = [
+            self.tokenizer.tokenize(sentence) for sentence in sentences
+        ]
         return self.encoder.encode_sentences(tokenized_sentences)
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="LASER: Download Laser models")
     parser.add_argument(