[numpy] Fix users of NumPy APIs that are removed in NumPy 2.0.

tf_keras/callbacks.py

@@ -1423,20 +1423,20 @@ def __init__(
         if mode == "min":
             self.monitor_op = np.less
             if self.best is None:
-                self.best = np.Inf
+                self.best = np.inf
         elif mode == "max":
             self.monitor_op = np.greater
             if self.best is None:
-                self.best = -np.Inf
+                self.best = -np.inf
         else:
             if "acc" in self.monitor or self.monitor.startswith("fmeasure"):
                 self.monitor_op = np.greater
                 if self.best is None:
-                    self.best = -np.Inf
+                    self.best = -np.inf
             else:
                 self.monitor_op = np.less
                 if self.best is None:
-                    self.best = np.Inf
+                    self.best = np.inf
 
         if self.save_freq != "epoch" and not isinstance(self.save_freq, int):
             raise ValueError(
@@ -2112,7 +2112,7 @@ def on_train_begin(self, logs=None):
         # Allow instances to be re-used
         self.wait = 0
         self.stopped_epoch = 0
-        self.best = np.Inf if self.monitor_op == np.less else -np.Inf
+        self.best = np.inf if self.monitor_op == np.less else -np.inf
         self.best_weights = None
         self.best_epoch = 0
 
@@ -3115,10 +3115,10 @@ def _reset(self):
             self.mode == "auto" and "acc" not in self.monitor
         ):
             self.monitor_op = lambda a, b: np.less(a, b - self.min_delta)
-            self.best = np.Inf
+            self.best = np.inf
         else:
             self.monitor_op = lambda a, b: np.greater(a, b + self.min_delta)
-            self.best = -np.Inf
+            self.best = -np.inf
         self.cooldown_counter = 0
         self.wait = 0
 

tf_keras/mixed_precision/autocast_variable_test.py

@@ -517,13 +517,13 @@ def test_assign_stays_in_true_dtype(self, distribution):
                 # same float16 value.
                 self.evaluate(x.assign(1.0 + small_tensor))
                 self.assertEqual(1.0, self.evaluate(x.value()))
-            self.assertEqual(1.0 + small_val, self.evaluate(x))
+            self.assertEqual(1.0 + np.float64(small_val), self.evaluate(x))
 
             self.evaluate(x.assign(1.0))
             with autocast_variable.enable_auto_cast_variables(tf.float16):
                 self.evaluate(x.assign_add(small_tensor))
                 self.assertEqual(1.0, self.evaluate(x.value()))
-            self.assertEqual(1.0 + small_val, self.evaluate(x))
+            self.assertEqual(1.0 + np.float64(small_val), self.evaluate(x))
 
     def test_thread_local_autocast_dtype(self):
         x = get_var(1.0, tf.float32)

tf_keras/utils/data_utils.py

@@ -1108,7 +1108,7 @@ def pad_sequences(
         maxlen = np.max(lengths)
 
     is_dtype_str = np.issubdtype(dtype, np.str_) or np.issubdtype(
-        dtype, np.unicode_
+        dtype, np.str_
     )
     if isinstance(value, str) and dtype != object and not is_dtype_str:
         raise ValueError(