Update quality tooling for formatting (#21480)

* Result of black 23.1

* Update target to Python 3.7

* Switch flake8 to ruff

* Configure isort

* Configure isort

* Apply isort with line limit

* Put the right black version

* adapt black in check copies

* Fix copies

examples/research_projects/pplm/run_pplm.py

@@ -30,10 +30,10 @@ from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
+from pplm_classification_head import ClassificationHead
 from torch import nn
 from tqdm import trange
 
-from pplm_classification_head import ClassificationHead
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 from transformers.file_utils import cached_path
 
@@ -345,7 +345,7 @@ def full_text_generation(
     gm_scale=0.9,
     kl_scale=0.01,
     repetition_penalty=1.0,
-    **kwargs
+    **kwargs,
 ):
     classifier, class_id = get_classifier(discrim, class_label, device)
 
@@ -463,7 +463,6 @@ def generate_text_pplm(
     unpert_discrim_loss = 0
     loss_in_time = []
     for i in trange(length, ascii=True):
-
         # Get past/probs for current output, except for last word
         # Note that GPT takes 2 inputs: past + current_token
 
@@ -547,7 +546,6 @@ def generate_text_pplm(
 
         # Fuse the modified model and original model
         if perturb:
-
             unpert_probs = nn.functional.softmax(unpert_logits[:, -1, :], dim=-1)
 
             pert_probs = (pert_probs**gm_scale) * (unpert_probs ** (1 - gm_scale))  # + SMALL_CONST

examples/research_projects/pplm/run_pplm_discrim_train.py

@@ -26,12 +26,12 @@ import torch
 import torch.optim as optim
 import torch.utils.data as data
 from nltk.tokenize.treebank import TreebankWordDetokenizer
+from pplm_classification_head import ClassificationHead
 from torch import nn
 from torchtext import data as torchtext_data
 from torchtext import datasets
 from tqdm import tqdm, trange
 
-from pplm_classification_head import ClassificationHead
 from transformers import GPT2LMHeadModel, GPT2Tokenizer