Add support for keras.layers.Embedding in _get_word_embedding_weight

This resolves:

- An issue of input embeddings in
  `TFT5ModelTest.test_resize_token_embeddings` and
  `TFT5ModelTest.test_save_load_after_resize_token_embeddings` when
  `model.config.tie_word_embeddings` is set to `False`:

  ```
  ValueError: Attempt to convert a value
  (<tf_keras.src.layers.core.embedding.Embedding object at 0x32b0747a0>)
  with an unsupported type
  (<class 'tf_keras.src.layers.core.embedding.Embedding'>) to a Tensor.
  ```

- An issue of input embeddings in
  `TFMistralModelTest.test_resize_token_embeddings` where resizing token
  embeddings caused the following error:

  ```
  ValueError: Attempt to convert a value (None) with an unsupported type
  (<class 'NoneType'>) to a Tensor.
  ```

src/transformers/modeling_tf_utils.py

@@ -2083,6 +2083,10 @@ def _get_word_embedding_weight(model, embedding_layer):
             # value has a shape (num_tokens, dim) then needs to be transposed
             return tf.Variable(tf.transpose(embeds))
 
+        embeds = getattr(embedding_layer, "weights", None)
+        if embeds is not None and len(embeds) > 0:
+            return embeds[0]
+
         # The reason why the attributes don't exist might be
         # because the model is not built, so retry getting
         # the argument after building the model
@@ -2102,6 +2106,10 @@ def _get_word_embedding_weight(model, embedding_layer):
             # value has a shape (num_tokens, dim) then needs to be transposed
             return tf.Variable(tf.transpose(embeds))
 
+        embeds = getattr(embedding_layer, "weights", None)
+        if embeds is not None and len(embeds) > 0:
+            return embeds[0]
+
         return None
 
     def _resize_token_embeddings(self, new_num_tokens):

src/transformers/models/mistral/modeling_tf_mistral.py

@@ -824,8 +824,17 @@ def set_input_embeddings(self, value):
     def get_output_embeddings(self):
         return self.lm_head
 
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
+    def set_output_embeddings(self, value):
+        self.lm_head = keras.layers.Dense(
+            shape_list(value)[0],
+            use_bias=False,
+            kernel_initializer=get_initializer(self.config.initializer_range),
+            name="lm_head",
+        )
+        # in a dense layer the kernel has a shape (last_dim, units), for us (dim, num_tokens)
+        # value has a shape (num_tokens, dim) then needs to be transposed
+        transposed_value = tf.transpose(value)
+        self.lm_head.kernel = tf.Variable(transposed_value)
 
     def set_decoder(self, decoder):
         self.model = decoder