Fix past CI (#20967)

* Fix for Past CI

* make style

* clean up

* unindent 2 blocks

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/bert/test_tokenization_bert_tf.py

@@ -4,15 +4,15 @@ from tempfile import TemporaryDirectory
 
 from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
 from transformers.models.bert.tokenization_bert import BertTokenizer
-from transformers.testing_utils import require_tensorflow_text, slow
+from transformers.testing_utils import require_tensorflow_text, require_tf, slow
 
 
-if is_tensorflow_text_available():
-    from transformers.models.bert import TFBertTokenizer
-
 if is_tf_available():
     import tensorflow as tf
 
+if is_tensorflow_text_available():
+    from transformers.models.bert import TFBertTokenizer
+
 
 TOKENIZER_CHECKPOINTS = ["bert-base-uncased", "bert-base-cased"]
 TINY_MODEL_CHECKPOINT = "hf-internal-testing/tiny-bert-tf-only"
@@ -32,6 +32,7 @@ if is_tf_available():
             return out["pooler_output"]
 
 
+@require_tf
 @require_tensorflow_text
 class BertTokenizationTest(unittest.TestCase):
     # The TF tokenizers are usually going to be used as pretrained tokenizers from existing model checkpoints,

tests/models/gpt2/test_tokenization_gpt2_tf.py

@@ -4,15 +4,15 @@ from tempfile import TemporaryDirectory
 
 from transformers import AutoConfig, TFGPT2LMHeadModel, is_keras_nlp_available, is_tf_available
 from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
-from transformers.testing_utils import require_keras_nlp, slow
+from transformers.testing_utils import require_keras_nlp, require_tf, slow
 
 
-if is_keras_nlp_available():
-    from transformers.models.gpt2 import TFGPT2Tokenizer
-
 if is_tf_available():
     import tensorflow as tf
 
+if is_keras_nlp_available():
+    from transformers.models.gpt2 import TFGPT2Tokenizer
+
 
 TOKENIZER_CHECKPOINTS = ["gpt2"]
 TINY_MODEL_CHECKPOINT = "gpt2"
@@ -40,6 +40,7 @@ if is_tf_available():
             return outputs
 
 
+@require_tf
 @require_keras_nlp
 class GPTTokenizationTest(unittest.TestCase):
     # The TF tokenizers are usually going to be used as pretrained tokenizers from existing model checkpoints,

tests/models/layoutlmv2/test_tokenization_layoutlmv2.py

@@ -38,7 +38,14 @@ from transformers.models.layoutlmv2.tokenization_layoutlmv2 import (
     _is_punctuation,
     _is_whitespace,
 )
-from transformers.testing_utils import is_pt_tf_cross_test, require_pandas, require_tokenizers, require_torch, slow
+from transformers.testing_utils import (
+    is_pt_tf_cross_test,
+    require_detectron2,
+    require_pandas,
+    require_tokenizers,
+    require_torch,
+    slow,
+)
 
 from ...test_tokenization_common import (
     SMALL_TRAINING_CORPUS,
@@ -1264,6 +1271,7 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
                 self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)
 
     @require_torch
+    @require_detectron2
     @slow
     def test_torch_encode_plus_sent_to_model(self):
         import torch

tests/pipelines/test_pipelines_object_detection.py

@@ -22,7 +22,15 @@ from transformers import (
     is_vision_available,
     pipeline,
 )
-from transformers.testing_utils import nested_simplify, require_tf, require_timm, require_torch, require_vision, slow
+from transformers.testing_utils import (
+    nested_simplify,
+    require_pytesseract,
+    require_tf,
+    require_timm,
+    require_torch,
+    require_vision,
+    slow,
+)
 
 from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
@@ -245,6 +253,7 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
         )
 
     @require_torch
+    @require_pytesseract
     @slow
     def test_layoutlm(self):
         model_id = "Narsil/layoutlmv3-finetuned-funsd"

tests/trainer/test_trainer.py

@@ -50,6 +50,7 @@ from transformers.testing_utils import (
     get_gpu_count,
     get_tests_dir,
     is_staging_test,
+    require_accelerate,
     require_intel_extension_for_pytorch,
     require_optuna,
     require_ray,
@@ -1285,6 +1286,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
             self.assertAlmostEqual(b, b1, delta=1e-5)
 
     @slow
+    @require_accelerate
     @require_torch_non_multi_gpu
     def test_auto_batch_size_finder(self):