diff --git a/examples/legacy/seq2seq/utils.py b/examples/legacy/seq2seq/utils.py
index 2e0586a269b..e207e4d0dbd 100644
--- a/examples/legacy/seq2seq/utils.py
+++ b/examples/legacy/seq2seq/utils.py
@@ -371,7 +371,7 @@ def sortish_sampler_indices(data: List, bs: int, shuffle=True) -> np.array:
     ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
     max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
     ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
-    sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int)
+    sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=int)
     sort_idx = np.concatenate((ck_idx[0], sort_idx))
     return sort_idx
 
diff --git a/examples/pytorch/benchmarking/plot_csv_file.py b/examples/pytorch/benchmarking/plot_csv_file.py
index 58dc50bb832..1a0ae735d8c 100644
--- a/examples/pytorch/benchmarking/plot_csv_file.py
+++ b/examples/pytorch/benchmarking/plot_csv_file.py
@@ -132,7 +132,7 @@ class Plot:
                 if self.args.plot_along_batch:
                     y_axis_array = np.asarray(
                         [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],
-                        dtype=np.int,
+                        dtype=int,
                     )
                 else:
                     y_axis_array = np.asarray(
@@ -144,7 +144,7 @@ class Plot:
                     ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                 )
 
-                x_axis_array = np.asarray(x_axis_array, np.int)[: len(y_axis_array)]
+                x_axis_array = np.asarray(x_axis_array, int)[: len(y_axis_array)]
                 plt.scatter(
                     x_axis_array, y_axis_array, label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"
                 )
diff --git a/examples/research_projects/seq2seq-distillation/utils.py b/examples/research_projects/seq2seq-distillation/utils.py
index b6994a1831d..a45194e6e05 100644
--- a/examples/research_projects/seq2seq-distillation/utils.py
+++ b/examples/research_projects/seq2seq-distillation/utils.py
@@ -353,7 +353,7 @@ def sortish_sampler_indices(data: List, bs: int, shuffle=True) -> np.array:
     ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
     max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
     ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0]  # then make sure it goes first.
-    sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=np.int)
+    sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=int)
     sort_idx = np.concatenate((ck_idx[0], sort_idx))
     return sort_idx
 
diff --git a/examples/tensorflow/benchmarking/plot_csv_file.py b/examples/tensorflow/benchmarking/plot_csv_file.py
index 58dc50bb832..1a0ae735d8c 100644
--- a/examples/tensorflow/benchmarking/plot_csv_file.py
+++ b/examples/tensorflow/benchmarking/plot_csv_file.py
@@ -132,7 +132,7 @@ class Plot:
                 if self.args.plot_along_batch:
                     y_axis_array = np.asarray(
                         [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results],
-                        dtype=np.int,
+                        dtype=int,
                     )
                 else:
                     y_axis_array = np.asarray(
@@ -144,7 +144,7 @@ class Plot:
                     ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
                 )
 
-                x_axis_array = np.asarray(x_axis_array, np.int)[: len(y_axis_array)]
+                x_axis_array = np.asarray(x_axis_array, int)[: len(y_axis_array)]
                 plt.scatter(
                     x_axis_array, y_axis_array, label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"
                 )