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/seq2seq-distillation/_test_bash_script.py

@@ -8,9 +8,9 @@ from unittest.mock import patch
 import pytorch_lightning as pl
 import timeout_decorator
 import torch
-
 from distillation import SummarizationDistiller, distill_main
 from finetune import SummarizationModule, main
+
 from transformers import MarianMTModel
 from transformers.file_utils import cached_path
 from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow

examples/research_projects/seq2seq-distillation/_test_make_student.py

@@ -2,6 +2,7 @@ import tempfile
 import unittest
 
 from make_student import create_student_by_copying_alternating_layers
+
 from transformers import AutoConfig
 from transformers.file_utils import cached_property
 from transformers.testing_utils import require_torch

examples/research_projects/seq2seq-distillation/_test_seq2seq_examples.py

@@ -5,18 +5,18 @@ import sys
 import tempfile
 from pathlib import Path
 
+import lightning_base
 import pytest
 import pytorch_lightning as pl
 import torch
-from torch import nn
-
-import lightning_base
 from convert_pl_checkpoint_to_hf import convert_pl_to_hf
 from distillation import distill_main
 from finetune import SummarizationModule, main
 from huggingface_hub import list_models
 from parameterized import parameterized
 from run_eval import generate_summaries_or_translations
+from torch import nn
+
 from transformers import AutoConfig, AutoModelForSeq2SeqLM
 from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
 from utils import label_smoothed_nll_loss, lmap, load_json

examples/research_projects/seq2seq-distillation/_test_seq2seq_examples_multi_gpu.py

@@ -98,7 +98,6 @@ class TestSummarizationDistillerMultiGPU(TestCasePlus):
 
     @require_torch_multi_gpu
     def test_multi_gpu(self):
-
         updates = dict(
             no_teacher=True,
             freeze_encoder=True,

examples/research_projects/seq2seq-distillation/distillation.py

@@ -9,11 +9,11 @@ from typing import List  # noqa: F401
 
 import pytorch_lightning as pl
 import torch
-from torch import nn
-
 from finetune import SummarizationModule, TranslationModule
 from finetune import main as ft_main
 from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
+from torch import nn
+
 from transformers import AutoModelForSeq2SeqLM, MBartTokenizer, T5ForConditionalGeneration
 from transformers.models.bart.modeling_bart import shift_tokens_right
 from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params

examples/research_projects/seq2seq-distillation/finetune.py

@@ -13,10 +13,10 @@ from typing import Dict, List, Tuple
 import numpy as np
 import pytorch_lightning as pl
 import torch
+from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
 from torch import nn
 from torch.utils.data import DataLoader
 
-from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
 from transformers import MBartTokenizer, T5ForConditionalGeneration
 from transformers.models.bart.modeling_bart import shift_tokens_right
 from utils import (

examples/research_projects/seq2seq-distillation/lightning_base.py

@@ -69,7 +69,7 @@ class BaseTransformer(pl.LightningModule):
         config=None,
         tokenizer=None,
         model=None,
-        **config_kwargs
+        **config_kwargs,
     ):
         """Initialize a model, tokenizer and config."""
         super().__init__()
@@ -346,7 +346,7 @@ def generic_train(
     extra_callbacks=[],
     checkpoint_callback=None,
     logging_callback=None,
-    **extra_train_kwargs
+    **extra_train_kwargs,
 ):
     pl.seed_everything(args.seed)
 

examples/research_projects/seq2seq-distillation/make_student.py

@@ -84,7 +84,7 @@ def create_student_by_copying_alternating_layers(
     copy_first_teacher_layers=False,
     e_layers_to_copy=None,
     d_layers_to_copy=None,
-    **extra_config_kwargs
+    **extra_config_kwargs,
 ) -> Tuple[PreTrainedModel, List[int], List[int]]:
     """Make a student by copying alternating layers from a teacher, save it to save_path.
     Args:
@@ -107,7 +107,6 @@ def create_student_by_copying_alternating_layers(
         AutoTokenizer.from_pretrained(teacher).save_pretrained(save_path)  # purely for convenience
         teacher = AutoModelForSeq2SeqLM.from_pretrained(teacher).eval()
     else:
-
         assert isinstance(teacher, PreTrainedModel), f"teacher must be a model or string got type {type(teacher)}"
     init_kwargs = teacher.config.to_diff_dict()
 

examples/research_projects/seq2seq-distillation/utils.py

@@ -15,10 +15,10 @@ import torch
 import torch.distributed as dist
 from rouge_score import rouge_scorer, scoring
 from sacrebleu import corpus_bleu
+from sentence_splitter import add_newline_to_end_of_each_sentence
 from torch import nn
 from torch.utils.data import Dataset, Sampler
 
-from sentence_splitter import add_newline_to_end_of_each_sentence
 from transformers import BartTokenizer, EvalPrediction, PreTrainedTokenizer, T5Tokenizer
 from transformers.file_utils import cached_property
 from transformers.models.bart.modeling_bart import shift_tokens_right
@@ -115,7 +115,7 @@ class AbstractSeq2SeqDataset(Dataset):
         type_path="train",
         n_obs=None,
         prefix="",
-        **dataset_kwargs
+        **dataset_kwargs,
     ):
         super().__init__()
         self.src_file = Path(data_dir).joinpath(type_path + ".source")