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/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)