[Styling] stylify using ruff (#27144)

* try to stylify using ruff

* might need to remove these changes?

* use ruf format andruff check

* use isinstance instead of type comparision

* use # fmt: skip

* use # fmt: skip

* nits

* soem styling changes

* update ci job

* nits isinstance

* more files update

* nits

* more nits

* small nits

* check and format

* revert wrong changes

* actually use formatter instead of checker

* nits

* well docbuilder is overwriting this commit

* revert notebook changes

* try to nuke docbuilder

* style

* fix feature exrtaction test

* remve `indent-width = 4`

* fixup

* more nits

* update the ruff version that we use

* style

* nuke docbuilder styling

* leve the print for detected changes

* nits

* Remove file I/O

Co-authored-by: charliermarsh
 <charlie.r.marsh@gmail.com>

* style

* nits

* revert notebook changes

* Add # fmt skip when possible

* Add # fmt skip when possible

* Fix

* More `  # fmt: skip` usage

* More `  # fmt: skip` usage

* More `  # fmt: skip` usage

* NIts

* more fixes

* fix tapas

* Another way to skip

* Recommended way

* Fix two more fiels

* Remove asynch
Remove asynch

---------

Co-authored-by: charliermarsh <charlie.r.marsh@gmail.com>

examples/research_projects/deebert/src/modeling_highway_bert.py

@@ -32,7 +32,7 @@ class DeeBertEncoder(nn.Module):
         self.early_exit_entropy = [-1 for _ in range(config.num_hidden_layers)]
 
     def set_early_exit_entropy(self, x):
-        if (type(x) is float) or (type(x) is int):
+        if isinstance(x, (float, int)):
             for i in range(len(self.early_exit_entropy)):
                 self.early_exit_entropy[i] = x
         else:
@@ -232,9 +232,7 @@ class DeeBertModel(BertPreTrainedModel):
         outputs = (
             sequence_output,
             pooled_output,
-        ) + encoder_outputs[
-            1:
-        ]  # add hidden_states and attentions if they are here
+        ) + 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/longform-qa/eli5_app.py

@@ -158,9 +158,7 @@ header_full = """
     </span>
   </body>
 </html>
-""" % (
-    header_html,
-)
+""" % (header_html,)
 st.sidebar.markdown(
     header_full,
     unsafe_allow_html=True,

examples/research_projects/lxmert/modeling_frcnn.py

@@ -1706,9 +1706,7 @@ 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(
+                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"

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

@@ -652,9 +652,7 @@ class MaskedBertModel(MaskedBertPreTrainedModel):
         outputs = (
             sequence_output,
             pooled_output,
-        ) + encoder_outputs[
-            1:
-        ]  # add hidden_states and attentions if they are here
+        ) + 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

@@ -311,8 +311,7 @@ def train(args, train_dataset, model, tokenizer, teacher=None):
             tr_loss += loss.item()
             if (step + 1) % args.gradient_accumulation_steps == 0 or (
                 # last step in epoch but step is always smaller than gradient_accumulation_steps
-                len(epoch_iterator) <= args.gradient_accumulation_steps
-                and (step + 1) == len(epoch_iterator)
+                len(epoch_iterator) <= args.gradient_accumulation_steps and (step + 1) == len(epoch_iterator)
             ):
                 if args.fp16:
                     nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)

examples/research_projects/quantization-qdqbert/quant_trainer.py

@@ -239,7 +239,7 @@ def print_model_summary(model, name_width=25, line_width=180, ignore=None):
             continue
         if type(mod) in ignore:
             continue
-        if [True for s in ignore if type(s) is str and s in name]:
+        if [True for s in ignore if isinstance(s, str) and s in name]:
             continue
         act_str = f"Act:{input_q.extra_repr()}"
         wgt_str = f"Wgt:{weight_q.extra_repr()}"

examples/research_projects/visual_bert/modeling_frcnn.py

@@ -1706,9 +1706,7 @@ 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(
+                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"