[tests] remove tests from libraries with deprecated support (flax, tensorflow_text, ...) (#39051)

* rm tf/flax tests

* more flax deletions

* revert fixture change

* reverted test that should not be deleted; rm tf/flax test

* revert

* fix a few add-model-like tests

* fix add-model-like checkpoint source

* a few more

* test_get_model_files_only_pt fix

* fix test_retrieve_info_for_model_with_xxx

* fix test_retrieve_model_classes

* relative paths are the devil

* add todo

tests/utils/test_modeling_utils.py

@@ -57,7 +57,6 @@ from transformers.testing_utils import (
     hub_retry,
     is_staging_test,
     require_accelerate,
-    require_flax,
     require_non_hpu,
     require_read_token,
     require_safetensors,
@@ -77,7 +76,6 @@ from transformers.utils import (
 from transformers.utils.import_utils import (
     is_flash_attn_2_available,
     is_flash_attn_3_available,
-    is_flax_available,
     is_torch_npu_available,
     is_torch_sdpa_available,
 )
@@ -317,10 +315,6 @@ class TestModelGammaBeta(PreTrainedModel):
         return self.LayerNorm()
 
 
-if is_flax_available():
-    from transformers import FlaxBertModel
-
-
 TINY_T5 = "patrickvonplaten/t5-tiny-random"
 TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
 TINY_MISTRAL = "hf-internal-testing/tiny-random-MistralForCausalLM"
@@ -1517,19 +1511,6 @@ class ModelUtilsTest(TestCasePlus):
         for p1, p2 in zip(model.parameters(), new_model.parameters()):
             self.assertTrue(torch.equal(p1, p2))
 
-    @require_safetensors
-    @require_flax
-    def test_safetensors_torch_from_flax(self):
-        hub_model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
-        model = FlaxBertModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            model.save_pretrained(tmp_dir, safe_serialization=True)
-            new_model = BertModel.from_pretrained(tmp_dir)
-
-        for p1, p2 in zip(hub_model.parameters(), new_model.parameters()):
-            self.assertTrue(torch.equal(p1, p2))
-
     @require_safetensors
     def test_safetensors_torch_from_torch_sharded(self):
         model = BertModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")