Make style

tests/data_tests/test_samplers.py

@@ -1,9 +1,11 @@
-from TTS.tts.datasets import load_tts_samples
-from TTS.config.shared_configs import BaseDatasetConfig
-from TTS.tts.utils.languages import get_language_weighted_sampler
-import torch
 import functools
 
+import torch
+
+from TTS.config.shared_configs import BaseDatasetConfig
+from TTS.tts.datasets import load_tts_samples
+from TTS.tts.utils.languages import get_language_weighted_sampler
+
 # Fixing random state to avoid random fails
 torch.manual_seed(0)
 
@@ -25,18 +27,19 @@ dataset_config_pt = BaseDatasetConfig(
 
 # Adding the EN samples twice to create an unbalanced dataset
 train_samples, eval_samples = load_tts_samples(
-    [dataset_config_en, dataset_config_en, dataset_config_pt],
-    eval_split=True
+    [dataset_config_en, dataset_config_en, dataset_config_pt], eval_split=True
 )
 
+
 def is_balanced(lang_1, lang_2):
-    return 0.85 < lang_1/lang_2 < 1.2
+    return 0.85 < lang_1 / lang_2 < 1.2
+
 
 random_sampler = torch.utils.data.RandomSampler(train_samples)
 ids = functools.reduce(lambda a, b: a + b, [list(random_sampler) for i in range(100)])
 en, pt = 0, 0
 for index in ids:
-    if train_samples[index][3] == 'en':
+    if train_samples[index][3] == "en":
         en += 1
     else:
         pt += 1
@@ -47,7 +50,7 @@ weighted_sampler = get_language_weighted_sampler(train_samples)
 ids = functools.reduce(lambda a, b: a + b, [list(weighted_sampler) for i in range(100)])
 en, pt = 0, 0
 for index in ids:
-    if train_samples[index][3] == 'en':
+    if train_samples[index][3] == "en":
         en += 1
     else:
         pt += 1