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[Styling] stylify using ruff (#27144)

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* 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>
This commit is contained in:
Arthur
2023-11-16 17:43:19 +01:00
committed by GitHub
parent acb5b4aff5
commit 651408a077
480 changed files with 867 additions and 1059 deletions
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4
examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py
View File

@@ -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)

3
examples/research_projects/movement-pruning/masked_run_glue.py
View File

@@ -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)