SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Avoid using uncessary get_values(MODEL_MAPPING) (#29362)

Browse Source 
* more fixes

* more fixes

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2024-02-29 17:19:17 +08:00
committed by GitHub
parent b647acdb53
commit 44fe1a1cc4
14 changed files with 94 additions and 85 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
tests/models/data2vec/test_modeling_data2vec_vision.py
View File

@@ -18,7 +18,6 @@
import unittest
from transformers import Data2VecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
@@ -32,11 +31,11 @@ if is_torch_available():
from torch import nn
from transformers import (
MODEL_MAPPING,
Data2VecVisionForImageClassification,
Data2VecVisionForSemanticSegmentation,
Data2VecVisionModel,
)
from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
from transformers.models.data2vec.modeling_data2vec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -235,7 +234,7 @@ class Data2VecVisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Te
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in [*get_values(MODEL_MAPPING)]:
if model_class.__name__ in MODEL_MAPPING_NAMES.values():
continue
model = model_class(config)
@@ -254,7 +253,7 @@ class Data2VecVisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Te
config.return_dict = True
for model_class in self.all_model_classes:
if model_class in [*get_values(MODEL_MAPPING)] or not model_class.supports_gradient_checkpointing:
if model_class.__name__ in MODEL_MAPPING_NAMES.values() or not model_class.supports_gradient_checkpointing:
continue
# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
# this can then be incorporated into _prepare_for_class in test_modeling_common.py