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Update ruff to 0.11.2 (#36962)

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* update

* update

* update

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2025-03-25 16:00:11 +01:00
committed by GitHub
parent bc1c90a755
commit c6814b4ee8
152 changed files with 604 additions and 609 deletions
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28
src/transformers/modeling_utils.py
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@@ -2509,9 +2509,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
total_decoder_name="",
total_encoder_name="",
):
assert isinstance(decoder_pointer, nn.Module) and isinstance(
encoder_pointer, nn.Module
), f"{decoder_pointer} and {encoder_pointer} have to be of type nn.Module"
assert isinstance(decoder_pointer, nn.Module) and isinstance(encoder_pointer, nn.Module), (
f"{decoder_pointer} and {encoder_pointer} have to be of type nn.Module"
)
if hasattr(decoder_pointer, "weight"):
assert hasattr(encoder_pointer, "weight")
encoder_pointer.weight = decoder_pointer.weight
@@ -2525,9 +2525,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
encoder_modules = encoder_pointer._modules
decoder_modules = decoder_pointer._modules
if len(decoder_modules) > 0:
assert (
len(encoder_modules) > 0
), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
assert len(encoder_modules) > 0, (
f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
)
all_encoder_weights = {module_name + "/" + sub_name for sub_name in encoder_modules.keys()}
encoder_layer_pos = 0
@@ -3571,7 +3571,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
f"Please upgrade accelerate with `pip install -U accelerate`"
)
# init state_dict for this shard
shard_state_dict = {name: "" for name in shard}
shard_state_dict = dict.fromkeys(shard, "")
for module_name in shard:
# skip to collect this weight again
if shard_state_dict.get(module_name) != "":
@@ -4814,7 +4814,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
param_device_map = expand_device_map(device_map, checkpoint_keys)
str_dtype = str(dtype).replace("torch.", "") if dtype is not None else "float32"
if sharded_metadata is None:
weight_map = {p: checkpoint_files[0] for p in checkpoint_keys}
weight_map = dict.fromkeys(checkpoint_keys, checkpoint_files[0])
else:
folder = os.path.sep.join(checkpoint_files[0].split(os.path.sep)[:-1])
# Fix the weight map keys according to the key mapping
@@ -5446,9 +5446,9 @@ class PoolerEndLogits(nn.Module):
Returns:
`torch.FloatTensor`: The end logits for SQuAD.
"""
assert (
start_states is not None or start_positions is not None
), "One of start_states, start_positions should be not None"
assert start_states is not None or start_positions is not None, (
"One of start_states, start_positions should be not None"
)
if start_positions is not None:
slen, hsz = hidden_states.shape[-2:]
start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
@@ -5514,9 +5514,9 @@ class PoolerAnswerClass(nn.Module):
"""
# No dependency on end_feature so that we can obtain one single `cls_logits` for each sample.
hsz = hidden_states.shape[-1]
assert (
start_states is not None or start_positions is not None
), "One of start_states, start_positions should be not None"
assert start_states is not None or start_positions is not None, (
"One of start_states, start_positions should be not None"
)
if start_positions is not None:
start_positions = start_positions[:, None, None].expand(-1, -1, hsz) # shape (bsz, 1, hsz)
start_states = hidden_states.gather(-2, start_positions).squeeze(-2) # shape (bsz, hsz)