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/adversarial/run_hans.py

@@ -22,6 +22,7 @@ from typing import Dict, List, Optional
 
 import numpy as np
 import torch
+from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
 
 import transformers
 from transformers import (
@@ -35,7 +36,6 @@ from transformers import (
     set_seed,
 )
 from transformers.trainer_utils import is_main_process
-from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
 
 
 logger = logging.getLogger(__name__)

examples/research_projects/adversarial/utils_hans.py

@@ -20,8 +20,8 @@ from dataclasses import dataclass
 from typing import List, Optional, Union
 
 import tqdm
-
 from filelock import FileLock
+
 from transformers import (
     BartTokenizer,
     BartTokenizerFast,
@@ -134,7 +134,6 @@ if is_torch_available():
             # and the others will use the cache.
             lock_path = cached_features_file + ".lock"
             with FileLock(lock_path):
-
                 if os.path.exists(cached_features_file) and not overwrite_cache:
                     logger.info(f"Loading features from cached file {cached_features_file}")
                     self.features = torch.load(cached_features_file)

examples/research_projects/bert-loses-patience/run_glue_with_pabee.py

@@ -25,14 +25,14 @@ import random
 
 import numpy as np
 import torch
+from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
+from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
 from torch import nn
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
 import transformers
-from pabee.modeling_pabee_albert import AlbertForSequenceClassificationWithPabee
-from pabee.modeling_pabee_bert import BertForSequenceClassificationWithPabee
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -173,7 +173,6 @@ def train(args, train_dataset, model, tokenizer):
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
-
             # Skip past any already trained steps if resuming training
             if steps_trained_in_current_epoch > 0:
                 steps_trained_in_current_epoch -= 1
@@ -263,7 +262,6 @@ def train(args, train_dataset, model, tokenizer):
 
 
 def evaluate(args, model, tokenizer, prefix="", patience=0):
-
     if args.model_type == "albert":
         model.albert.set_regression_threshold(args.regression_threshold)
         model.albert.set_patience(patience)
@@ -736,7 +734,6 @@ def main():
         logger.info("Evaluate the following checkpoints: %s", checkpoints)
 
         for checkpoint in checkpoints:
-
             global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
             prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
 

examples/research_projects/bert-loses-patience/test_run_glue_with_pabee.py

@@ -4,6 +4,7 @@ import sys
 from unittest.mock import patch
 
 import run_glue_with_pabee
+
 from transformers.testing_utils import TestCasePlus
 
 

examples/research_projects/bertabs/convert_bertabs_original_pytorch_checkpoint.py

@@ -24,9 +24,9 @@ import logging
 from collections import namedtuple
 
 import torch
-
 from model_bertabs import BertAbsSummarizer
 from models.model_builder import AbsSummarizer  # The authors' implementation
+
 from transformers import BertTokenizer
 
 

examples/research_projects/bertabs/modeling_bertabs.py

@@ -24,10 +24,10 @@ import math
 
 import numpy as np
 import torch
+from configuration_bertabs import BertAbsConfig
 from torch import nn
 from torch.nn.init import xavier_uniform_
 
-from configuration_bertabs import BertAbsConfig
 from transformers import BertConfig, BertModel, PreTrainedModel
 
 

examples/research_projects/bertabs/run_summarization.py

@@ -6,10 +6,10 @@ import sys
 from collections import namedtuple
 
 import torch
+from modeling_bertabs import BertAbs, build_predictor
 from torch.utils.data import DataLoader, SequentialSampler
 from tqdm import tqdm
 
-from modeling_bertabs import BertAbs, build_predictor
 from transformers import BertTokenizer
 
 from .utils_summarization import (
@@ -45,7 +45,6 @@ def evaluate(args):
         generated_summaries = []
 
         import nltk
-
         import rouge
 
         nltk.download("punkt")

examples/research_projects/codeparrot/examples/train_complexity_predictor.py

@@ -3,8 +3,8 @@ from copy import deepcopy
 
 import numpy as np
 from datasets import ClassLabel, DatasetDict, load_dataset
-
 from evaluate import load
+
 from transformers import (
     AutoModelForSequenceClassification,
     AutoTokenizer,

examples/research_projects/codeparrot/scripts/bpe_training.py

@@ -1,7 +1,7 @@
+from arguments import TokenizerTrainingArguments
 from datasets import load_dataset
 from tqdm import tqdm
 
-from arguments import TokenizerTrainingArguments
 from transformers import AutoTokenizer, HfArgumentParser
 from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
 

examples/research_projects/codeparrot/scripts/codeparrot_training.py

@@ -6,16 +6,16 @@ from pathlib import Path
 
 import datasets
 import torch
+from accelerate import Accelerator, DistributedType
+from arguments import TrainingArguments
 from datasets import load_dataset
+from huggingface_hub import Repository
 from torch.optim import AdamW
 from torch.utils.data import IterableDataset
 from torch.utils.data.dataloader import DataLoader
 from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
 
 import transformers
-from accelerate import Accelerator, DistributedType
-from arguments import TrainingArguments
-from huggingface_hub import Repository
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
 
 

examples/research_projects/codeparrot/scripts/human_eval.py

@@ -5,15 +5,15 @@ import re
 from collections import defaultdict
 
 import torch
+from accelerate import Accelerator
+from accelerate.utils import set_seed
+from arguments import HumanEvalArguments
 from datasets import load_dataset, load_metric
 from torch.utils.data import IterableDataset
 from torch.utils.data.dataloader import DataLoader
 from tqdm import tqdm
 
 import transformers
-from accelerate import Accelerator
-from accelerate.utils import set_seed
-from arguments import HumanEvalArguments
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
 
 

examples/research_projects/codeparrot/scripts/initialize_model.py

@@ -1,4 +1,5 @@
 from arguments import InitializationArguments
+
 from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
 
 

examples/research_projects/codeparrot/scripts/minhash_deduplication.py

@@ -6,10 +6,9 @@ from functools import partial
 from typing import Dict, List, Optional, Set, Tuple, Type
 
 from datasets import Dataset
-from tqdm import tqdm
-
 from datasketch import MinHash, MinHashLSH
 from dpu_utils.utils.iterators import ThreadedIterator
+from tqdm import tqdm
 
 
 NON_ALPHA = re.compile("[^A-Za-z_0-9]")

examples/research_projects/codeparrot/scripts/preprocessing.py

@@ -9,10 +9,10 @@ import time
 from pathlib import Path
 
 import numpy as np
-from datasets import load_dataset
-
 from arguments import PreprocessingArguments
+from datasets import load_dataset
 from minhash_deduplication import deduplicate_dataset
+
 from transformers import AutoTokenizer, HfArgumentParser
 
 

examples/research_projects/codeparrot/scripts/pretokenizing.py

@@ -1,9 +1,9 @@
 import multiprocessing
 import time
 
+from arguments import PretokenizationArguments
 from datasets import load_dataset
 
-from arguments import PretokenizationArguments
 from transformers import AutoTokenizer, HfArgumentParser
 
 

examples/research_projects/codeparrot/scripts/tests/test_deduplicate.py

@@ -1,7 +1,6 @@
 from unittest import TestCase
 
 from datasets import Dataset
-
 from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
 
 

examples/research_projects/codeparrot/scripts/validation_loss.py

@@ -1,12 +1,12 @@
 import logging
 
 import torch
+from accelerate import Accelerator
+from arguments import EvaluationArguments
 from datasets import load_dataset
 from torch.utils.data import IterableDataset
 from torch.utils.data.dataloader import DataLoader
 
-from accelerate import Accelerator
-from arguments import EvaluationArguments
 from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
 
 

examples/research_projects/decision_transformer/run_decision_transformer.py

@@ -1,8 +1,8 @@
+import gym
 import numpy as np
 import torch
-
-import gym
 from mujoco_py import GlfwContext
+
 from transformers import DecisionTransformerModel
 
 

examples/research_projects/deebert/src/modeling_highway_bert.py

@@ -229,7 +229,10 @@ class DeeBertModel(BertPreTrainedModel):
         sequence_output = encoder_outputs[0]
         pooled_output = self.pooler(sequence_output)
 
-        outputs = (sequence_output, pooled_output,) + encoder_outputs[
+        outputs = (
+            sequence_output,
+            pooled_output,
+        ) + encoder_outputs[
             1:
         ]  # add hidden_states and attentions if they are here
         return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

examples/research_projects/deebert/src/modeling_highway_roberta.py

@@ -19,7 +19,6 @@ from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayExc
     ROBERTA_START_DOCSTRING,
 )
 class DeeRobertaModel(DeeBertModel):
-
     config_class = RobertaConfig
     base_model_prefix = "roberta"
 
@@ -36,7 +35,6 @@ class DeeRobertaModel(DeeBertModel):
     ROBERTA_START_DOCSTRING,
 )
 class DeeRobertaForSequenceClassification(BertPreTrainedModel):
-
     config_class = RobertaConfig
     base_model_prefix = "roberta"
 

examples/research_projects/deebert/test_glue_deebert.py

@@ -4,6 +4,7 @@ import sys
 from unittest.mock import patch
 
 import run_glue_deebert
+
 from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
 
 
@@ -45,7 +46,6 @@ class DeeBertTests(TestCasePlus):
     @slow
     @require_torch_non_multi_gpu
     def test_glue_deebert_train(self):
-
         train_args = """
             --model_type roberta
             --model_name_or_path roberta-base

examples/research_projects/distillation/distiller.py

@@ -21,14 +21,14 @@ import time
 
 import psutil
 import torch
+from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
+from lm_seqs_dataset import LmSeqsDataset
 from torch import nn
 from torch.optim import AdamW
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm
 
-from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
-from lm_seqs_dataset import LmSeqsDataset
 from transformers import get_linear_schedule_with_warmup
 from utils import logger
 

examples/research_projects/distillation/run_squad_w_distillation.py

@@ -189,7 +189,6 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
-
             # Skip past any already trained steps if resuming training
             if steps_trained_in_current_epoch > 0:
                 steps_trained_in_current_epoch -= 1

examples/research_projects/distillation/train.py

@@ -24,9 +24,9 @@ import shutil
 
 import numpy as np
 import torch
-
 from distiller import Distiller
 from lm_seqs_dataset import LmSeqsDataset
+
 from transformers import (
     BertConfig,
     BertForMaskedLM,

examples/research_projects/information-gain-filtration/igf/igf.py

@@ -5,13 +5,13 @@ import copy
 import logging
 import random
 
+import joblib
 import numpy as np
 import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
-import joblib
 from transformers import AdamW, GPT2LMHeadModel, get_linear_schedule_with_warmup
 
 
@@ -119,7 +119,6 @@ def recopy_gpt2(orig_model, device, max_steps):
 
 
 def intermittent_save(contexts, real_perps, past_perps, filename):
-
     """
     save the perplexity differences to filename
 
@@ -152,7 +151,6 @@ def collect_objective_set(
     filename="dev.jbl",
     recopy_model=recopy_gpt2,
 ):
-
     """
     Collect individual IGF values from pre-trained transformer model
     max_steps samples of training data to train secondary model
@@ -271,7 +269,6 @@ def generate_datasets(
 def train_secondary_learner(
     secondary_learner, train_dataset, max_epochs, batch_size, eval_freq=50, igf_model_path="secondary_learner.pt"
 ):
-
     """
     Train the secondary learner (igf_model)
 

examples/research_projects/information-gain-filtration/run_clm_igf.py

@@ -28,11 +28,9 @@ Last, a plot is generated to compare the performance of IGF to standard fine-tun
 import argparse
 import random
 
+import joblib
 import numpy as np
 import torch
-from torch.utils.data import DataLoader, RandomSampler
-
-import joblib
 from igf.igf import (
     SecondaryLearner,
     collect_objective_set,
@@ -43,6 +41,8 @@ from igf.igf import (
     set_seed,
     train_secondary_learner,
 )
+from torch.utils.data import DataLoader, RandomSampler
+
 from transformers import GPT2LMHeadModel
 
 
@@ -55,7 +55,6 @@ def generate_n_pairs(
     data_file="data/tokenized_stories_train_wikitext103.jbl",
     igf_data_file="igf_context_pairs.jbl",
 ):
-
     """
     Collecting *n* pairs for training the secondary learner
     Args:

examples/research_projects/jax-projects/big_bird/bigbird_flax.py

@@ -4,8 +4,6 @@ from dataclasses import dataclass
 from functools import partial
 from typing import Callable
 
-from tqdm.auto import tqdm
-
 import flax.linen as nn
 import jax
 import jax.numpy as jnp
@@ -16,6 +14,8 @@ from flax import jax_utils, struct, traverse_util
 from flax.serialization import from_bytes, to_bytes
 from flax.training import train_state
 from flax.training.common_utils import shard
+from tqdm.auto import tqdm
+
 from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
 from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
 
@@ -98,7 +98,6 @@ class Args:
 
 @dataclass
 class DataCollator:
-
     pad_id: int
     max_length: int = 4096  # no dynamic padding on TPUs
 

examples/research_projects/jax-projects/big_bird/evaluate.py

@@ -1,8 +1,8 @@
-from datasets import load_from_disk
-
 import jax
 import jax.numpy as jnp
 from bigbird_flax import FlaxBigBirdForNaturalQuestions
+from datasets import load_from_disk
+
 from transformers import BigBirdTokenizerFast
 
 

examples/research_projects/jax-projects/big_bird/prepare_natural_questions.py

@@ -1,10 +1,9 @@
 import os
 
+import jsonlines
 import numpy as np
 from tqdm import tqdm
 
-import jsonlines
-
 
 DOC_STRIDE = 2048
 MAX_LENGTH = 4096

examples/research_projects/jax-projects/big_bird/train.py

@@ -1,12 +1,12 @@
 import os
 from dataclasses import replace
 
-from datasets import load_dataset
-
 import jax
 import wandb
 from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
+from datasets import load_dataset
 from flax import jax_utils
+
 from transformers import BigBirdTokenizerFast
 
 

examples/research_projects/jax-projects/dataset-streaming/run_mlm_flax_stream.py

@@ -32,17 +32,17 @@ from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
 import datasets
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
 import flax
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
+from datasets import load_dataset
 from flax import jax_utils, traverse_util
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
+from tqdm import tqdm
+
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_MASKED_LM_MAPPING,

examples/research_projects/jax-projects/hybrid_clip/modeling_hybrid_clip.py

@@ -20,6 +20,7 @@ import jax
 import jax.numpy as jnp
 from configuration_hybrid_clip import HybridCLIPConfig
 from flax.core.frozen_dict import FrozenDict
+
 from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
 from transformers.modeling_flax_utils import FlaxPreTrainedModel
 from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
@@ -132,7 +133,7 @@ class FlaxHybridCLIP(FlaxPreTrainedModel):
         input_shape: Optional[Tuple] = None,
         seed: int = 0,
         dtype: jnp.dtype = jnp.float32,
-        **kwargs
+        **kwargs,
     ):
         if input_shape is None:
             input_shape = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

examples/research_projects/jax-projects/hybrid_clip/run_hybrid_clip.py

@@ -32,22 +32,22 @@ from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Callable, Optional
 
+import jax
+import jax.numpy as jnp
+import optax
 import torch
+from flax import jax_utils
+from flax.jax_utils import unreplicate
+from flax.training import train_state
+from flax.training.common_utils import get_metrics, shard, shard_prng_key
+from modeling_hybrid_clip import FlaxHybridCLIP
 from torchvision.datasets import VisionDataset
 from torchvision.io import ImageReadMode, read_image
 from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
 from torchvision.transforms.functional import InterpolationMode
 from tqdm import tqdm
 
-import jax
-import jax.numpy as jnp
-import optax
 import transformers
-from flax import jax_utils
-from flax.jax_utils import unreplicate
-from flax.training import train_state
-from flax.training.common_utils import get_metrics, shard, shard_prng_key
-from modeling_hybrid_clip import FlaxHybridCLIP
 from transformers import AutoTokenizer, HfArgumentParser, TrainingArguments, is_tensorboard_available, set_seed
 
 

examples/research_projects/jax-projects/model_parallel/run_clm_mp.py

@@ -28,19 +28,19 @@ from pathlib import Path
 from typing import Callable, Optional
 
 import datasets
-import numpy as np
-from datasets import Dataset, load_dataset
-from tqdm import tqdm
-
 import jax
 import jax.numpy as jnp
+import numpy as np
 import optax
-import transformers
+from datasets import Dataset, load_dataset
 from flax.core.frozen_dict import freeze, unfreeze
 from flax.training.common_utils import onehot, stack_forest
 from jax.experimental.maps import mesh
 from jax.experimental.pjit import pjit
 from partitions import set_partitions
+from tqdm import tqdm
+
+import transformers
 from transformers import (
     CONFIG_MAPPING,
     FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,

examples/research_projects/jax-projects/wav2vec2/run_wav2vec2_pretrain_flax.py

@@ -6,18 +6,18 @@ from dataclasses import field
 from pathlib import Path
 from typing import Dict, List, Optional, Union
 
-import numpy as np
-from datasets import DatasetDict, load_dataset
-from tqdm import tqdm
-
 import flax
 import jax
 import jax.numpy as jnp
 import librosa
+import numpy as np
 import optax
+from datasets import DatasetDict, load_dataset
 from flax import jax_utils, traverse_util
 from flax.training import train_state
 from flax.training.common_utils import get_metrics, onehot, shard
+from tqdm import tqdm
+
 from transformers import (
     FlaxWav2Vec2ForPreTraining,
     HfArgumentParser,

examples/research_projects/longform-qa/eli5_app.py

@@ -1,11 +1,9 @@
 import datasets
+import faiss
 import numpy as np
 import streamlit as st
 import torch
 from elasticsearch import Elasticsearch
-
-import faiss
-import transformers
 from eli5_utils import (
     embed_questions_for_retrieval,
     make_qa_s2s_model,
@@ -13,6 +11,8 @@ from eli5_utils import (
     query_es_index,
     query_qa_dense_index,
 )
+
+import transformers
 from transformers import AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer
 
 

examples/research_projects/longform-qa/eli5_utils.py

@@ -5,6 +5,7 @@ from random import choice, randint
 from time import time
 
 import datasets  # noqa: F401
+import faiss  # noqa: F401
 import numpy as np
 import pandas as pd
 import torch
@@ -15,7 +16,6 @@ from torch import nn
 from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
 from tqdm import tqdm
 
-import faiss  # noqa: F401
 from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup
 
 

examples/research_projects/luke/run_luke_ner_no_trainer.py

@@ -27,14 +27,14 @@ from pathlib import Path
 
 import datasets
 import torch
+from accelerate import Accelerator, DistributedDataParallelKwargs
 from datasets import ClassLabel, load_dataset, load_metric
+from huggingface_hub import Repository
+from luke_utils import DataCollatorForLukeTokenClassification, is_punctuation, padding_tensor
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
 
 import transformers
-from accelerate import Accelerator, DistributedDataParallelKwargs
-from huggingface_hub import Repository
-from luke_utils import DataCollatorForLukeTokenClassification, is_punctuation, padding_tensor
 from transformers import (
     AdamW,
     LukeConfig,

examples/research_projects/lxmert/extracting_data.py

@@ -9,9 +9,9 @@ from collections import OrderedDict
 import datasets
 import numpy as np
 import torch
-
 from modeling_frcnn import GeneralizedRCNN
 from processing_image import Preprocess
+
 from utils import Config
 
 

examples/research_projects/lxmert/modeling_frcnn.py

@@ -169,7 +169,6 @@ def get_norm(norm, out_channels):
 
 
 def _create_grid_offsets(size: List[int], stride: int, offset: float, device):
-
     grid_height, grid_width = size
     shifts_x = torch.arange(
         offset * stride,
@@ -390,7 +389,6 @@ def assign_boxes_to_levels(
     canonical_box_size: int,
     canonical_level: int,
 ):
-
     box_sizes = torch.sqrt(torch.cat([boxes.area() for boxes in box_lists]))
     # Eqn.(1) in FPN paper
     level_assignments = torch.floor(canonical_level + torch.log2(box_sizes / canonical_box_size + 1e-8))
@@ -1708,9 +1706,10 @@ class GeneralizedRCNN(nn.Module):
             elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
                 archive_file = pretrained_model_name_or_path
             elif os.path.isfile(pretrained_model_name_or_path + ".index"):
-                assert from_tf, (
-                    "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint"
-                    .format(pretrained_model_name_or_path + ".index")
+                assert (
+                    from_tf
+                ), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format(
+                    pretrained_model_name_or_path + ".index"
                 )
                 archive_file = pretrained_model_name_or_path + ".index"
             else:

examples/research_projects/lxmert/utils.py

@@ -34,14 +34,13 @@ from pathlib import Path
 from urllib.parse import urlparse
 from zipfile import ZipFile, is_zipfile
 
-import numpy as np
-from PIL import Image
-from tqdm.auto import tqdm
-
 import cv2
+import numpy as np
 import requests
 import wget
 from filelock import FileLock
+from PIL import Image
+from tqdm.auto import tqdm
 from yaml import Loader, dump, load
 
 
@@ -181,7 +180,6 @@ class Config:
 
     @classmethod
     def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs):
-
         cache_dir = kwargs.pop("cache_dir", None)
         force_download = kwargs.pop("force_download", False)
         resume_download = kwargs.pop("resume_download", False)
@@ -225,14 +223,13 @@ class Config:
 
 # quick compare tensors
 def compare(in_tensor):
-
     out_tensor = torch.load("dump.pt", map_location=in_tensor.device)
     n1 = in_tensor.numpy()
     n2 = out_tensor.numpy()[0]
     print(n1.shape, n1[0, 0, :5])
     print(n2.shape, n2[0, 0, :5])
     assert np.allclose(n1, n2, rtol=0.01, atol=0.1), (
-        f"{sum([1 for x in np.isclose(n1, n2, rtol=0.01, atol=0.1).flatten() if x == False])/len(n1.flatten())*100:.4f} %"
+        f"{sum([1 for x in np.isclose(n1, n2, rtol=0.01, atol=0.1).flatten() if x is False])/len(n1.flatten())*100:.4f} %"
         " element-wise mismatch"
     )
     raise Exception("tensors are all good")
@@ -300,7 +297,6 @@ def get_from_cache(
     user_agent=None,
     local_files_only=False,
 ):
-
     if cache_dir is None:
         cache_dir = TRANSFORMERS_CACHE
     if isinstance(cache_dir, Path):
@@ -355,7 +351,6 @@ def get_from_cache(
     # Prevent parallel downloads of the same file with a lock.
     lock_path = cache_path + ".lock"
     with FileLock(lock_path):
-
         # If the download just completed while the lock was activated.
         if os.path.exists(cache_path) and not force_download:
             # Even if returning early like here, the lock will be released.
@@ -406,7 +401,6 @@ def get_from_cache(
 
 
 def url_to_filename(url, etag=None):
-
     url_bytes = url.encode("utf-8")
     url_hash = sha256(url_bytes)
     filename = url_hash.hexdigest()

examples/research_projects/lxmert/visualizing_image.py

@@ -18,6 +18,7 @@
 import colorsys
 import io
 
+import cv2
 import matplotlib as mpl
 import matplotlib.colors as mplc
 import matplotlib.figure as mplfigure
@@ -25,7 +26,6 @@ import numpy as np
 import torch
 from matplotlib.backends.backend_agg import FigureCanvasAgg
 
-import cv2
 from utils import img_tensorize
 
 

examples/research_projects/mlm_wwm/run_chinese_ref.py

@@ -3,6 +3,7 @@ import json
 from typing import List
 
 from ltp import LTP
+
 from transformers.models.bert.tokenization_bert import BertTokenizer
 
 

examples/research_projects/mm-imdb/run_mmimdb.py

@@ -30,6 +30,7 @@ from torch import nn
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
+from utils_mmimdb import ImageEncoder, JsonlDataset, collate_fn, get_image_transforms, get_mmimdb_labels
 
 import transformers
 from transformers import (
@@ -43,7 +44,6 @@ from transformers import (
     get_linear_schedule_with_warmup,
 )
 from transformers.trainer_utils import is_main_process
-from utils_mmimdb import ImageEncoder, JsonlDataset, collate_fn, get_image_transforms, get_mmimdb_labels
 
 
 try:

examples/research_projects/movement-pruning/bertarize.py

@@ -22,7 +22,6 @@ import os
 import shutil
 
 import torch
-
 from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
 
 

examples/research_projects/movement-pruning/counts_parameters.py

@@ -19,7 +19,6 @@ import argparse
 import os
 
 import torch
-
 from emmental.modules import ThresholdBinarizer, TopKBinarizer
 
 

examples/research_projects/movement-pruning/emmental/configuration_bert_masked.py

@@ -50,7 +50,7 @@ class MaskedBertConfig(PretrainedConfig):
         pruning_method="topK",
         mask_init="constant",
         mask_scale=0.0,
-        **kwargs
+        **kwargs,
     ):
         super().__init__(pad_token_id=pad_token_id, **kwargs)
 

examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py

@@ -649,7 +649,10 @@ class MaskedBertModel(MaskedBertPreTrainedModel):
         sequence_output = encoder_outputs[0]
         pooled_output = self.pooler(sequence_output)
 
-        outputs = (sequence_output, pooled_output,) + encoder_outputs[
+        outputs = (
+            sequence_output,
+            pooled_output,
+        ) + encoder_outputs[
             1:
         ]  # add hidden_states and attentions if they are here
         return outputs  # sequence_output, pooled_output, (hidden_states), (attentions)

examples/research_projects/movement-pruning/masked_run_glue.py

@@ -24,12 +24,12 @@ import random
 
 import numpy as np
 import torch
+from emmental import MaskedBertConfig, MaskedBertForSequenceClassification
 from torch import nn
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
-from emmental import MaskedBertConfig, MaskedBertForSequenceClassification
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -228,7 +228,6 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
-
             # Skip past any already trained steps if resuming training
             if steps_trained_in_current_epoch > 0:
                 steps_trained_in_current_epoch -= 1

examples/research_projects/movement-pruning/masked_run_squad.py

@@ -25,12 +25,12 @@ import timeit
 
 import numpy as np
 import torch
+from emmental import MaskedBertConfig, MaskedBertForQuestionAnswering
 from torch import nn
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
-from emmental import MaskedBertConfig, MaskedBertForQuestionAnswering
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -236,7 +236,6 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
     for _ in train_iterator:
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
-
             # Skip past any already trained steps if resuming training
             if steps_trained_in_current_epoch > 0:
                 steps_trained_in_current_epoch -= 1

examples/research_projects/onnx/summarization/bart_onnx/generation_onnx.py

@@ -264,7 +264,6 @@ class BARTGenerator(torch.nn.Module, GenerationMixin):
 
         past: List[torch.Tensor] = []
         while cur_len < max_length:
-
             logits, past = self._decoder_forward(input_ids, encoder_output, attention_mask, past)
             next_token_logits = logits[:, -1, :]
 
@@ -303,7 +302,6 @@ class BARTGenerator(torch.nn.Module, GenerationMixin):
         decoder_start_token_id,
         bos_token_id: Optional[int] = None,
     ) -> torch.LongTensor:
-
         decoder_input_ids = (
             torch.ones((input_ids.shape[0], 1), dtype=input_ids.dtype, device=input_ids.device)
             * decoder_start_token_id
@@ -633,7 +631,6 @@ class BARTBeamSearchGenerator(BARTGenerator):
     def beam_search(
         self, input_ids, encoder_output, attention_mask, num_beams, max_length, pad_token_id: int, eos_token_id: int
     ):
-
         batch_size = self.beam_scorer.batch_size
 
         num_beams = self.beam_scorer.num_beams

examples/research_projects/onnx/summarization/bart_onnx/reduce_onnx_size.py

@@ -5,7 +5,6 @@ Code to remove duplicate initializers to reduce ONNX model size.
 import os
 
 import numpy
-
 import onnx
 
 

examples/research_projects/onnx/summarization/run_onnx_exporter.py

@@ -22,12 +22,12 @@ import os
 import sys
 
 import numpy as np
-import torch
-
 import onnxruntime
-import transformers
+import torch
 from bart_onnx.generation_onnx import BARTBeamSearchGenerator
 from bart_onnx.reduce_onnx_size import remove_dup_initializers
+
+import transformers
 from transformers import BartForConditionalGeneration, BartTokenizer
 
 

examples/research_projects/performer/modeling_flax_performer.py

@@ -15,13 +15,13 @@
 
 from typing import Callable, Dict, Tuple
 
-import numpy as np
-
 import flax.linen as nn
 import jax
 import jax.numpy as jnp
+import numpy as np
 from jax.random import PRNGKey
 from modeling_flax_performer_utils import make_fast_softmax_attention
+
 from transformers.file_utils import add_start_docstrings
 from transformers.modeling_flax_utils import ACT2FN
 from transformers.models.bert.configuration_bert import BertConfig
@@ -366,7 +366,6 @@ class FlaxPerformerModel(FlaxBertPreTrainedModel):
 
             # SelfAttention needs also to replace "weight" by "kernel"
             if {"query", "key", "value"} & key_parts:
-
                 # Flax SelfAttention decomposes the heads (num_head, size // num_heads)
                 if "bias" in key:
                     jax_state[key] = tensor.reshape((config.num_attention_heads, -1))
@@ -443,7 +442,6 @@ class FlaxPerformerModel(FlaxBertPreTrainedModel):
     def __call__(
         self, input_ids, token_type_ids=None, position_ids=None, dropout_rng: PRNGKey = None, attention_mask=None
     ):
-
         input_ids, attention_mask, token_type_ids, position_ids = self._check_inputs(
             input_ids, attention_mask, token_type_ids, position_ids
         )

examples/research_projects/performer/modeling_flax_performer_utils.py

@@ -30,11 +30,10 @@ import abc
 import functools
 from collections.abc import Iterable  # pylint: disable=g-importing-member
 
-import numpy as onp
-from absl import logging
-
 import jax
 import jax.numpy as jnp
+import numpy as onp
+from absl import logging
 from jax import lax, random
 
 
@@ -524,7 +523,6 @@ class FastAttentionviaLowRankDecomposition(FastAttention):
         deterministic=False,
         precision=None,
     ):
-
         assert key.shape[:-1] == value.shape[:-1]
         assert query.shape[0:1] == key.shape[0:1] and query.shape[-1] == key.shape[-1]
         if axis is None:

examples/research_projects/performer/run_mlm_performer.py

@@ -28,18 +28,18 @@ from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Dict, List, Optional, Tuple
 
-import numpy as np
-from datasets import load_dataset
-from tqdm import tqdm
-
 import jax
 import jax.numpy as jnp
+import numpy as np
+from datasets import load_dataset
 from flax import jax_utils
 from flax.optim import Adam
 from flax.training import common_utils
 from flax.training.common_utils import get_metrics
 from jax.nn import log_softmax
 from modeling_flax_performer import FlaxPerformerForMaskedLM
+from tqdm import tqdm
+
 from transformers import (
     MODEL_FOR_MASKED_LM_MAPPING,
     AutoTokenizer,
@@ -632,7 +632,6 @@ if __name__ == "__main__":
 
     epochs = tqdm(range(nb_epochs), desc=f"Epoch ... (1/{nb_epochs})", position=0)
     for epoch in epochs:
-
         # ======================== Training ================================
         # Create sampling rng
         rng, training_rng, eval_rng = jax.random.split(rng, 3)

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
 
 

examples/research_projects/quantization-qdqbert/evaluate-hf-trt-qa.py

@@ -21,19 +21,19 @@ import timeit
 
 import datasets
 import numpy as np
-import torch
-from absl import logging as absl_logging
-from datasets import load_dataset, load_metric
-from torch.utils.data import DataLoader
-
 import pycuda.autoinit  # noqa: F401
 import pycuda.driver as cuda
 import tensorrt as trt
-import transformers
+import torch
+from absl import logging as absl_logging
 from accelerate import Accelerator
+from datasets import load_dataset, load_metric
+from torch.utils.data import DataLoader
+from utils_qa import postprocess_qa_predictions
+
+import transformers
 from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
 from transformers.trainer_pt_utils import nested_concat, nested_truncate
-from utils_qa import postprocess_qa_predictions
 
 
 TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
@@ -395,7 +395,6 @@ logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
 with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
     f.read()
 ) as engine, engine.create_execution_context() as context:
-
     # setup for TRT inferrence
     for i in range(len(input_names)):
         context.set_binding_shape(i, INPUT_SHAPE)
@@ -427,7 +426,6 @@ with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.d
 
     all_preds = None
     for step, batch in enumerate(eval_dataloader):
-
         outputs, infer_time = model_infer(batch, context, d_inputs, h_output0, h_output1, d_output0, d_output1, stream)
         total_time += infer_time
         niter += 1

examples/research_projects/quantization-qdqbert/ort-infer-benchmark.py

@@ -2,7 +2,6 @@ import os
 import time
 
 import numpy as np
-
 import onnxruntime as ort
 
 

examples/research_projects/quantization-qdqbert/quant_trainer.py

@@ -16,10 +16,9 @@
 import logging
 import re
 
-import torch
-
 import pytorch_quantization
 import pytorch_quantization.nn as quant_nn
+import torch
 from pytorch_quantization import calib
 from pytorch_quantization.tensor_quant import QuantDescriptor
 

examples/research_projects/quantization-qdqbert/run_quant_qa.py

@@ -26,11 +26,12 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
-from datasets import load_dataset, load_metric
-
 import quant_trainer
-import transformers
+from datasets import load_dataset, load_metric
 from trainer_quant_qa import QuestionAnsweringTrainer
+from utils_qa import postprocess_qa_predictions
+
+import transformers
 from transformers import (
     AutoTokenizer,
     DataCollatorWithPadding,
@@ -46,7 +47,6 @@ from transformers import (
 from transformers.trainer_utils import SchedulerType, get_last_checkpoint
 from transformers.utils import check_min_version
 from transformers.utils.versions import require_version
-from utils_qa import postprocess_qa_predictions
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.

examples/research_projects/quantization-qdqbert/trainer_quant_qa.py

@@ -20,10 +20,10 @@ A subclass of `Trainer` specific to Question-Answering tasks
 import logging
 import os
 
+import quant_trainer
 import torch
 from torch.utils.data import DataLoader
 
-import quant_trainer
 from transformers import Trainer, is_torch_tpu_available
 from transformers.trainer_utils import PredictionOutput
 

examples/research_projects/rag-end2end-retriever/callbacks_rag.py

@@ -6,7 +6,6 @@ import pytorch_lightning as pl
 import torch
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.utilities import rank_zero_only
-
 from utils_rag import save_json
 
 

examples/research_projects/rag-end2end-retriever/distributed_ray_retriever.py

@@ -2,6 +2,7 @@ import logging
 import random
 
 import ray
+
 from transformers import RagConfig, RagRetriever, RagTokenizer
 from transformers.models.rag.retrieval_rag import CustomHFIndex
 
@@ -166,7 +167,6 @@ class RagRayDistributedRetriever(RagRetriever):
         )
 
     def re_load(self):
-
         logger.info("re-loading the new dataset with embeddings")
         # access from the training loop
 

examples/research_projects/rag-end2end-retriever/finetune_rag.py

@@ -252,14 +252,12 @@ class GenerativeQAModule(BaseTransformer):
         raise NotImplementedError("pad not implemented")
 
     def training_step(self, batch, batch_idx) -> Dict:
-
         global isEmUpdateBusy  # use to check whether the entire embedding update process is finished or not
         global isAddIndexBusy  # use to check whether the entire indexing process  is finished or not
         global processes  # use to keep threads embedding update processes
         global threadHandle_index  # use to keep thread in embedding indexing processes
 
         if (self.trainer.global_rank == 0) and (self.custom_config.end2end):
-
             if (not batch_idx == 0) and (batch_idx % self.custom_config.indexing_freq == 0):
                 free_gpu_list = []
                 nvmlInit()
@@ -282,7 +280,6 @@ class GenerativeQAModule(BaseTransformer):
                     has_free_gpus = False
 
                 if (not isEmUpdateBusy) and has_free_gpus:
-
                     model_copy = type(self.model.rag.ctx_encoder)(
                         self.config_dpr
                     )  # get a new instance  #this will be load in the CPU
@@ -336,10 +333,8 @@ class GenerativeQAModule(BaseTransformer):
 
             # check when index building has started
             if isAddIndexBusy:
-
                 # check still the index_building process is happening
                 if not threadHandle_index.is_alive():
-
                     logger.info("Merging the dataset shards")
                     saved_dataset_shards = []
 
@@ -494,7 +489,6 @@ class GenerativeQAModule(BaseTransformer):
         self.tokenizer.save_pretrained(save_path)
 
         if self.custom_config.end2end:
-
             modified_state_dict = self.model.state_dict()
             for key in self.model.state_dict().keys():
                 if key.split(".")[1] == "ctx_encoder":
@@ -803,7 +797,6 @@ def main(args=None, model=None) -> GenerativeQAModule:
 
 
 if __name__ == "__main__":
-
     multiprocessing.set_start_method("spawn")
     parser = argparse.ArgumentParser()
     parser = pl.Trainer.add_argparse_args(parser)

examples/research_projects/rag-end2end-retriever/kb_encode_utils.py

@@ -2,9 +2,9 @@ import os
 from functools import partial
 from glob import glob
 
+import faiss
 from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk
 
-import faiss
 from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
 
 
@@ -26,7 +26,6 @@ def split_documents(documents):
 
 
 def embed_update(ctx_encoder, total_processes, device, process_num, shard_dir, csv_path):
-
     kb_dataset = load_dataset(
         "csv", data_files=[csv_path], split="train", delimiter="\t", column_names=["title", "text"]
     )

examples/research_projects/rag-end2end-retriever/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__()
@@ -365,7 +365,7 @@ 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/rag-end2end-retriever/use_own_knowledge_dataset.py

@@ -6,10 +6,10 @@ from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import List, Optional
 
+import faiss
 import torch
 from datasets import Features, Sequence, Value, load_dataset
 
-import faiss
 from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
 
 
@@ -49,7 +49,6 @@ def main(
     processing_args: "ProcessingArguments",
     index_hnsw_args: "IndexHnswArguments",
 ):
-
     ######################################
     logger.info("Step 1 - Create the dataset")
     ######################################

examples/research_projects/rag/_test_finetune_rag.py

@@ -5,6 +5,7 @@ import sys
 from pathlib import Path
 
 import finetune_rag
+
 from transformers.file_utils import is_apex_available
 from transformers.testing_utils import (
     TestCasePlus,

examples/research_projects/rag/callbacks_rag.py

@@ -6,7 +6,6 @@ import pytorch_lightning as pl
 import torch
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 from pytorch_lightning.utilities import rank_zero_only
-
 from utils_rag import save_json
 
 

examples/research_projects/rag/consolidate_rag_checkpoint.py

@@ -17,7 +17,6 @@ def consolidate(
     generator_tokenizer_name_or_path: str = None,
     question_encoder_tokenizer_name_or_path: str = None,
 ):
-
     if config_name_or_path is None:
         config_name_or_path = "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"
 

examples/research_projects/rag/distributed_ray_retriever.py

@@ -2,6 +2,7 @@ import logging
 import random
 
 import ray
+
 from transformers import RagConfig, RagRetriever, RagTokenizer
 from transformers.models.rag.retrieval_rag import CustomHFIndex
 

examples/research_projects/rag/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__()
@@ -356,7 +356,7 @@ 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/rag/test_distributed_retriever.py

@@ -7,10 +7,10 @@ import unittest
 from unittest import TestCase
 from unittest.mock import patch
 
+import faiss
 import numpy as np
 from datasets import Dataset
 
-import faiss
 from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
 from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
 from transformers.integrations import is_ray_available

examples/research_projects/rag/use_own_knowledge_dataset.py

@@ -6,10 +6,10 @@ from pathlib import Path
 from tempfile import TemporaryDirectory
 from typing import List, Optional
 
+import faiss
 import torch
 from datasets import Features, Sequence, Value, load_dataset
 
-import faiss
 from transformers import (
     DPRContextEncoder,
     DPRContextEncoderTokenizerFast,
@@ -56,7 +56,6 @@ def main(
     processing_args: "ProcessingArguments",
     index_hnsw_args: "IndexHnswArguments",
 ):
-
     ######################################
     logger.info("Step 1 - Create the dataset")
     ######################################

examples/research_projects/robust-speech-event/eval.py

@@ -36,7 +36,6 @@ def log_results(result: Dataset, args: Dict[str, str]):
         target_file = f"log_{dataset_id}_targets.txt"
 
         with open(pred_file, "w") as p, open(target_file, "w") as t:
-
             # mapping function to write output
             def write_to_file(batch, i):
                 p.write(f"{i}" + "\n")

examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py

@@ -25,12 +25,12 @@ import warnings
 from dataclasses import dataclass, field
 from typing import Dict, List, Optional, Union
 
+import bitsandbytes as bnb
 import datasets
 import numpy as np
 import torch
 from datasets import DatasetDict, load_dataset, load_metric
 
-import bitsandbytes as bnb
 import transformers
 from transformers import (
     AutoConfig,
@@ -717,7 +717,6 @@ def main():
 
     # Training
     if training_args.do_train:
-
         # use last checkpoint if exist
         if last_checkpoint is not None:
             checkpoint = last_checkpoint

examples/research_projects/robust-speech-event/run_speech_recognition_ctc_streaming.py

@@ -622,7 +622,6 @@ def main():
 
     # Training
     if training_args.do_train:
-
         # use last checkpoint if exist
         if last_checkpoint is not None:
             checkpoint = last_checkpoint

examples/research_projects/self-training-text-classification/selftraining.py

@@ -23,12 +23,12 @@ import shutil
 from typing import List, Optional
 
 import datasets
+from accelerate import Accelerator
 from datasets import load_dataset
+from finetuning import finetune
 from tqdm.auto import tqdm
 
 import transformers
-from accelerate import Accelerator
-from finetuning import finetune
 from transformers import AutoConfig, set_seed
 from transformers.trainer_utils import IntervalStrategy
 

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")

examples/research_projects/tapex/run_wikisql_with_tapex.py

@@ -32,9 +32,10 @@ import nltk  # Here to have a nice missing dependency error message early on
 import numpy as np
 import pandas as pd
 from datasets import load_dataset
+from filelock import FileLock
+from wikisql_utils import _TYPE_CONVERTER, retrieve_wikisql_query_answer_tapas
 
 import transformers
-from filelock import FileLock
 from transformers import (
     AutoConfig,
     BartForConditionalGeneration,
@@ -48,7 +49,6 @@ from transformers import (
 from transformers.file_utils import is_offline_mode
 from transformers.trainer_utils import get_last_checkpoint, is_main_process
 from transformers.utils import check_min_version
-from wikisql_utils import _TYPE_CONVERTER, retrieve_wikisql_query_answer_tapas
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.

examples/research_projects/tapex/run_wikitablequestions_with_tapex.py

@@ -31,9 +31,9 @@ import nltk  # Here to have a nice missing dependency error message early on
 import numpy as np
 import pandas as pd
 from datasets import load_dataset
+from filelock import FileLock
 
 import transformers
-from filelock import FileLock
 from transformers import (
     AutoConfig,
     BartForConditionalGeneration,

examples/research_projects/visual_bert/extracting_data.py

@@ -9,9 +9,9 @@ from collections import OrderedDict
 import datasets
 import numpy as np
 import torch
-
 from modeling_frcnn import GeneralizedRCNN
 from processing_image import Preprocess
+
 from utils import Config
 
 

examples/research_projects/visual_bert/modeling_frcnn.py

@@ -169,7 +169,6 @@ def get_norm(norm, out_channels):
 
 
 def _create_grid_offsets(size: List[int], stride: int, offset: float, device):
-
     grid_height, grid_width = size
     shifts_x = torch.arange(
         offset * stride,
@@ -390,7 +389,6 @@ def assign_boxes_to_levels(
     canonical_box_size: int,
     canonical_level: int,
 ):
-
     box_sizes = torch.sqrt(torch.cat([boxes.area() for boxes in box_lists]))
     # Eqn.(1) in FPN paper
     level_assignments = torch.floor(canonical_level + torch.log2(box_sizes / canonical_box_size + 1e-8))
@@ -1708,9 +1706,10 @@ class GeneralizedRCNN(nn.Module):
             elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
                 archive_file = pretrained_model_name_or_path
             elif os.path.isfile(pretrained_model_name_or_path + ".index"):
-                assert from_tf, (
-                    "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint"
-                    .format(pretrained_model_name_or_path + ".index")
+                assert (
+                    from_tf
+                ), "We found a TensorFlow checkpoint at {}, please set from_tf to True to load from this checkpoint".format(
+                    pretrained_model_name_or_path + ".index"
                 )
                 archive_file = pretrained_model_name_or_path + ".index"
             else:

examples/research_projects/visual_bert/utils.py

@@ -34,14 +34,13 @@ from pathlib import Path
 from urllib.parse import urlparse
 from zipfile import ZipFile, is_zipfile
 
-import numpy as np
-from PIL import Image
-from tqdm.auto import tqdm
-
 import cv2
+import numpy as np
 import requests
 import wget
 from filelock import FileLock
+from PIL import Image
+from tqdm.auto import tqdm
 from yaml import Loader, dump, load
 
 
@@ -181,7 +180,6 @@ class Config:
 
     @classmethod
     def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs):
-
         cache_dir = kwargs.pop("cache_dir", None)
         force_download = kwargs.pop("force_download", False)
         resume_download = kwargs.pop("resume_download", False)
@@ -225,14 +223,13 @@ class Config:
 
 # quick compare tensors
 def compare(in_tensor):
-
     out_tensor = torch.load("dump.pt", map_location=in_tensor.device)
     n1 = in_tensor.numpy()
     n2 = out_tensor.numpy()[0]
     print(n1.shape, n1[0, 0, :5])
     print(n2.shape, n2[0, 0, :5])
     assert np.allclose(n1, n2, rtol=0.01, atol=0.1), (
-        f"{sum([1 for x in np.isclose(n1, n2, rtol=0.01, atol=0.1).flatten() if x == False])/len(n1.flatten())*100:.4f} %"
+        f"{sum([1 for x in np.isclose(n1, n2, rtol=0.01, atol=0.1).flatten() if x is False])/len(n1.flatten())*100:.4f} %"
         " element-wise mismatch"
     )
     raise Exception("tensors are all good")
@@ -300,7 +297,6 @@ def get_from_cache(
     user_agent=None,
     local_files_only=False,
 ):
-
     if cache_dir is None:
         cache_dir = TRANSFORMERS_CACHE
     if isinstance(cache_dir, Path):
@@ -355,7 +351,6 @@ def get_from_cache(
     # Prevent parallel downloads of the same file with a lock.
     lock_path = cache_path + ".lock"
     with FileLock(lock_path):
-
         # If the download just completed while the lock was activated.
         if os.path.exists(cache_path) and not force_download:
             # Even if returning early like here, the lock will be released.
@@ -406,7 +401,6 @@ def get_from_cache(
 
 
 def url_to_filename(url, etag=None):
-
     url_bytes = url.encode("utf-8")
     url_hash = sha256(url_bytes)
     filename = url_hash.hexdigest()

examples/research_projects/visual_bert/visualizing_image.py

@@ -18,6 +18,7 @@
 import colorsys
 import io
 
+import cv2
 import matplotlib as mpl
 import matplotlib.colors as mplc
 import matplotlib.figure as mplfigure
@@ -25,7 +26,6 @@ import numpy as np
 import torch
 from matplotlib.backends.backend_agg import FigureCanvasAgg
 
-import cv2
 from utils import img_tensorize
 
 

examples/research_projects/vqgan-clip/VQGAN_CLIP.py

@@ -1,15 +1,15 @@
 import os
 from glob import glob
 
+import imageio
 import torch
 import torchvision
-from PIL import Image
-from torch import nn
-
-import imageio
 import wandb
 from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
 from loaders import load_vqgan
+from PIL import Image
+from torch import nn
+
 from transformers import CLIPModel, CLIPTokenizerFast
 from utils import get_device, get_timestamp, show_pil
 

examples/research_projects/vqgan-clip/loaders.py

@@ -1,7 +1,6 @@
 import importlib
 
 import torch
-
 import yaml
 from omegaconf import OmegaConf
 from taming.models.vqgan import VQModel

examples/research_projects/wav2vec2/alignment.py

@@ -176,7 +176,6 @@ class Wav2Vec2Aligner:
                 out_align.write(str(seg) + "\n")
 
     def align_data(self, wav_dir, text_file, output_dir):
-
         if not os.path.exists(output_dir):
             os.makedirs(output_dir)
 

examples/research_projects/wav2vec2/run_asr.py

@@ -7,13 +7,13 @@ from dataclasses import dataclass, field
 from typing import Any, Callable, Dict, List, Optional, Set, Union
 
 import datasets
+import librosa
 import numpy as np
 import torch
+from lang_trans import arabic
 from packaging import version
 from torch import nn
 
-import librosa
-from lang_trans import arabic
 from transformers import (
     HfArgumentParser,
     Trainer,

examples/research_projects/wav2vec2/run_pretrain.py

@@ -4,12 +4,12 @@ import sys
 from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional, Union
 
+import librosa
 import torch
 from datasets import DatasetDict, load_dataset
 from packaging import version
 from torch import nn
 
-import librosa
 from transformers import (
     HfArgumentParser,
     Trainer,