Fix: unpin flake8 and fix cs errors (#4367)

* Fix: unpin flake8 and fix cs errors

* Ok we still need to quote those

src/transformers/convert_marian_to_pytorch.py

@@ -226,7 +226,7 @@ def lmap(f, x) -> List:
 def fetch_test_set(test_set_url):
     import wget
 
-    fname = wget.download(test_set_url, f"opus_test.txt")
+    fname = wget.download(test_set_url, "opus_test.txt")
     lns = Path(fname).open().readlines()
     src = lmap(str.strip, lns[::4])
     gold = lmap(str.strip, lns[1::4])

src/transformers/data/datasets/glue.py

@@ -114,7 +114,7 @@ class GlueDataset(Dataset):
                 torch.save(self.features, cached_features_file)
                 # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                 logger.info(
-                    f"Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
+                    "Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
                 )
 
     def __len__(self):

src/transformers/data/datasets/language_modeling.py

@@ -65,7 +65,7 @@ class TextDataset(Dataset):
                 with open(cached_features_file, "wb") as handle:
                     pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)
                 logger.info(
-                    f"Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
+                    "Saving features into cached file %s [took %.3f s]", cached_features_file, time.time() - start
                 )
 
     def __len__(self):

src/transformers/pipelines.py

@@ -24,7 +24,7 @@ from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from itertools import chain
 from os.path import abspath, exists
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
+from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
 
@@ -58,6 +58,10 @@ if is_torch_available():
         AutoModelWithLMHead,
     )
 
+if TYPE_CHECKING:
+    from .modeling_utils import PreTrainedModel
+    from .modeling_tf_utils import TFPreTrainedModel
+
 
 logger = logging.getLogger(__name__)