cross platform from_pretrained (#20538)

* add support for `from_pt`

* add tf_flax utility file

* Update src/transformers/modeling_tf_flax_utils.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* remove flax related modifications

* add test

* remove FLAX related commits

* fixup

* remove safetensor todos

* revert deletion

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

tests/test_modeling_tf_common.py

@@ -2127,6 +2127,14 @@ class UtilsFunctionsTest(unittest.TestCase):
             for p1, p2 in zip(model.weights, ref_model.weights):
                 assert np.allclose(p1.numpy(), p2.numpy())
 
+    @is_pt_tf_cross_test
+    def test_checkpoint_sharding_hub_from_pt(self):
+        model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded", from_pt=True)
+        # the model above is the same as the model below, just a sharded pytorch version.
+        ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
+        for p1, p2 in zip(model.weights, ref_model.weights):
+            assert np.allclose(p1.numpy(), p2.numpy())
+
     def test_shard_checkpoint(self):
         # This is the model we will use, total size 340,000 bytes.
         model = tf.keras.Sequential(