[tests] remove pt_tf equivalence tests (#36253)

tests/models/groupvit/test_modeling_groupvit.py

@@ -24,7 +24,7 @@ import numpy as np
 import requests
 
 from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig
-from transformers.testing_utils import is_flaky, is_pt_tf_cross_test, require_torch, require_vision, slow, torch_device
+from transformers.testing_utils import is_flaky, require_torch, require_vision, slow, torch_device
 from transformers.utils import is_torch_available, is_vision_available
 
 from ...test_configuration_common import ConfigTester
@@ -166,18 +166,6 @@ class GroupViTVisionModelTest(ModelTesterMixin, unittest.TestCase):
     def test_batching_equivalence(self):
         super().test_batching_equivalence()
 
-    @is_pt_tf_cross_test
-    def test_pt_tf_model_equivalence(self):
-        import tensorflow as tf
-
-        seed = 338
-        random.seed(seed)
-        np.random.seed(seed)
-        torch.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-        tf.random.set_seed(seed)
-        return super().test_pt_tf_model_equivalence()
-
     def test_model_get_set_embeddings(self):
         config, _ = self.model_tester.prepare_config_and_inputs_for_common()
 
@@ -595,22 +583,6 @@ class GroupViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
     def test_model_get_set_embeddings(self):
         pass
 
-    # overwritten from parent as this equivalent test needs a specific `seed` and hard to get a good one!
-    def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-5, name="outputs", attributes=None):
-        super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol=tol, name=name, attributes=attributes)
-
-    @is_pt_tf_cross_test
-    def test_pt_tf_model_equivalence(self):
-        import tensorflow as tf
-
-        seed = 163
-        random.seed(seed)
-        np.random.seed(seed)
-        torch.manual_seed(seed)
-        torch.cuda.manual_seed_all(seed)
-        tf.random.set_seed(seed)
-        return super().test_pt_tf_model_equivalence()
-
     # override as the `logit_scale` parameter initilization is different for GROUPVIT
     def test_initialization(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()