Black 20 release

examples/text-generation/pplm/run_pplm.py

@@ -698,7 +698,9 @@ def run_pplm_example(
                 for word_id in pert_gen_tok_text.tolist()[0]:
                     if word_id in bow_word_ids:
                         pert_gen_text += "{}{}{}".format(
-                            colorama.Fore.RED, tokenizer.decode([word_id]), colorama.Style.RESET_ALL,
+                            colorama.Fore.RED,
+                            tokenizer.decode([word_id]),
+                            colorama.Style.RESET_ALL,
                         )
                     else:
                         pert_gen_text += tokenizer.decode([word_id])
@@ -729,7 +731,10 @@ if __name__ == "__main__":
     parser.add_argument("--cond_text", type=str, default="The lake", help="Prefix texts to condition on")
     parser.add_argument("--uncond", action="store_true", help="Generate from end-of-text as prefix")
     parser.add_argument(
-        "--num_samples", type=int, default=1, help="Number of samples to generate from the modified latents",
+        "--num_samples",
+        type=int,
+        default=1,
+        help="Number of samples to generate from the modified latents",
     )
     parser.add_argument(
         "--bag_of_words",
@@ -751,13 +756,22 @@ if __name__ == "__main__":
         help="Discriminator to use",
     )
     parser.add_argument(
-        "--discrim_weights", type=str, default=None, help="Weights for the generic discriminator",
+        "--discrim_weights",
+        type=str,
+        default=None,
+        help="Weights for the generic discriminator",
     )
     parser.add_argument(
-        "--discrim_meta", type=str, default=None, help="Meta information for the generic discriminator",
+        "--discrim_meta",
+        type=str,
+        default=None,
+        help="Meta information for the generic discriminator",
     )
     parser.add_argument(
-        "--class_label", type=int, default=-1, help="Class label used for the discriminator",
+        "--class_label",
+        type=int,
+        default=-1,
+        help="Class label used for the discriminator",
     )
     parser.add_argument("--length", type=int, default=100)
     parser.add_argument("--stepsize", type=float, default=0.02)
@@ -773,7 +787,10 @@ if __name__ == "__main__":
         help="Length of past which is being optimized; 0 corresponds to infinite window length",
     )
     parser.add_argument(
-        "--horizon_length", type=int, default=1, help="Length of future to optimize over",
+        "--horizon_length",
+        type=int,
+        default=1,
+        help="Length of future to optimize over",
     )
     parser.add_argument("--decay", action="store_true", help="whether to decay or not")
     parser.add_argument("--gamma", type=float, default=1.5)
@@ -783,7 +800,10 @@ if __name__ == "__main__":
     parser.add_argument("--no_cuda", action="store_true", help="no cuda")
     parser.add_argument("--colorama", action="store_true", help="colors keywords")
     parser.add_argument(
-        "--repetition_penalty", type=float, default=1.0, help="Penalize repetition. More than 1.0 -> less repetition",
+        "--repetition_penalty",
+        type=float,
+        default=1.0,
+        help="Penalize repetition. More than 1.0 -> less repetition",
     )
 
     args = parser.parse_args()

examples/text-generation/pplm/run_pplm_discrim_train.py

@@ -242,7 +242,12 @@ def train_discriminator(
 
         text = torchtext_data.Field()
         label = torchtext_data.Field(sequential=False)
-        train_data, val_data, test_data = datasets.SST.splits(text, label, fine_grained=True, train_subtrees=True,)
+        train_data, val_data, test_data = datasets.SST.splits(
+            text,
+            label,
+            fine_grained=True,
+            train_subtrees=True,
+        )
 
         x = []
         y = []

examples/text-generation/run_generation.py

@@ -41,7 +41,9 @@ from transformers import (
 
 
 logging.basicConfig(
-    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO,
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    level=logging.INFO,
 )
 logger = logging.getLogger(__name__)
 
@@ -197,7 +199,10 @@ def main():
     args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
 
     logger.warning(
-        "device: %s, n_gpu: %s, 16-bits training: %s", args.device, args.n_gpu, args.fp16,
+        "device: %s, n_gpu: %s, 16-bits training: %s",
+        args.device,
+        args.n_gpu,
+        args.fp16,
     )
 
     set_seed(args)