diff --git a/src/transformers/models/sam/modeling_tf_sam.py b/src/transformers/models/sam/modeling_tf_sam.py
index 794587cdde0..46710b32984 100644
--- a/src/transformers/models/sam/modeling_tf_sam.py
+++ b/src/transformers/models/sam/modeling_tf_sam.py
@@ -226,7 +226,8 @@ class TFSamAttention(tf.keras.layers.Layer):
         batch, n_heads, n_tokens, c_per_head = shape_list(hidden_states)
         hidden_states = tf.transpose(hidden_states, perm=[0, 2, 1, 3])
         return tf.reshape(
-            hidden_states, (batch // max(1, point_batch_size), point_batch_size, n_tokens, n_heads * c_per_head)
+            hidden_states,
+            (batch // tf.reduce_max([1, point_batch_size]), point_batch_size, n_tokens, n_heads * c_per_head),
         )
 
     def call(self, query: tf.Tensor, key: tf.Tensor, value: tf.Tensor) -> tf.Tensor:
@@ -509,7 +510,7 @@ class TFSamMaskDecoder(tf.keras.layers.Layer):
         # Matt: The original Torch code checked that the sum of sparse_prompt_embeddings equalled 0. However, this only
         #       happens when the sparse prompt embeddings are an empty tensor with shape[1] == 0. I replaced
         #       it with an explicit shape check to avoid data-dependent control flow which breaks XLA.
-        if sparse_prompt_embeddings.shape[1] != 0:
+        if shape_list(sparse_prompt_embeddings)[1] != 0:
             tokens = tf.concat((output_tokens, sparse_prompt_embeddings), axis=2)
         else:
             tokens = output_tokens
@@ -695,8 +696,8 @@ class TFSamPromptEncoder(tf.keras.layers.Layer):
         """Embeds point prompts."""
         points = points + 0.5  # Shift to center of pixel
         if pad:
-            target_point_shape = (points.shape[0], points.shape[1], 1, points.shape[-1])
-            target_labels_shape = (points.shape[0], points.shape[1], 1)
+            target_point_shape = (shape_list(points)[0], shape_list(points)[1], 1, shape_list(points)[-1])
+            target_labels_shape = (shape_list(points)[0], shape_list(points)[1], 1)
             padding_point = tf.zeros(target_point_shape, dtype=points.dtype)
             padding_label = -tf.ones(target_labels_shape, dtype=labels.dtype)
             points = tf.concat([points, padding_point], axis=2)
@@ -722,12 +723,12 @@ class TFSamPromptEncoder(tf.keras.layers.Layer):
     def _embed_boxes(self, boxes: tf.Tensor) -> tf.Tensor:
         """Embeds box prompts."""
         boxes = boxes + 0.5  # Shift to center of pixel
-        batch_size, nb_boxes = boxes.shape[:2]
+        batch_size, nb_boxes = shape_list(boxes)[:2]
         coords = tf.reshape(boxes, (batch_size, nb_boxes, 2, 2))
         input_shape = (self.input_image_size, self.input_image_size)
         corner_embedding = self.shared_embedding(coords, input_shape)
         corner_embedding += tf.where(
-            tf.range(corner_embedding.shape[2])[None, None, :, None] == 0,
+            tf.range(shape_list(corner_embedding)[2])[None, None, :, None] == 0,
             self.point_embed[2][0],
             self.point_embed[3][0],
         )
@@ -754,7 +755,7 @@ class TFSamPromptEncoder(tf.keras.layers.Layer):
         """
         sparse_embeddings = None
         if input_points is not None:
-            batch_size, point_batch_size = input_points.shape[:2]
+            batch_size, point_batch_size = shape_list(input_points)[:2]
             if input_labels is None:
                 raise ValueError("If points are provided, labels must also be provided.")
             point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None))
@@ -763,7 +764,7 @@ class TFSamPromptEncoder(tf.keras.layers.Layer):
             )
             sparse_embeddings = tf.concat([sparse_embeddings, point_embeddings], axis=2)
         if input_boxes is not None:
-            batch_size = input_boxes.shape[0]
+            batch_size = shape_list(input_boxes)[0]
             box_embeddings = self._embed_boxes(input_boxes)
             if sparse_embeddings is None:
                 sparse_embeddings = box_embeddings
@@ -1376,8 +1377,8 @@ class TFSamModel(TFSamPreTrainedModel):
                 " got {}.".format(input_boxes.shape),
             )
         if input_points is not None and input_boxes is not None:
-            point_batch_size = input_points.shape[1]
-            box_batch_size = input_boxes.shape[1]
+            point_batch_size = shape_list(input_points)[1]
+            box_batch_size = shape_list(input_boxes)[1]
             if point_batch_size != box_batch_size:
                 raise ValueError(
                     "You should provide as many bounding boxes as input points per box. Got {} and {}.".format(