Explicitly convert NumPy ndarray of np.bool to Python bool

In NumPy 2 (and possibly earlier versions), lines 478-480 produced a
deprecation warning:

```
DeprecationWarning: In future, it will be an error
for 'np.bool' scalars to be interpreted as an index
```

This warning is somewhat misleading: it _is_ the case that Booleans
are involved, but they are not being used as indices.

The fields `rs`, `xs`, and `zs` of CliffordTableau as defined in file
`cirq-core/cirq/qis/clifford_tableau.py` have type
`Optional[np.ndarray]`, and the values in the ndarray have NumPy type
`bool` in practice. The protocol buffer version of CliffordTableau
defined in file `cirq-google/cirq_google/api/v2/program_pb2.pyi`
defines those fields as `collections.abc.Iterable[builtins.bool]`. At
first blush, you might think they're arrays of Booleans in both cases,
but unfortunately, there's a wrinkle: Python defines its built-in
`bool` type as being derived from `int` (see PEP 285), while NumPy
explicitly does _not_ drive its `bool` from its integer class (see
<https://numpy.org/doc/2.0/reference/arrays.scalars.html#numpy.bool>).
The warning about converting `np.bool` to index values (i.e.,
integers) probably arises when the `np.bool` values in the ndarray are
coerced into Python Booleans.

At first, I thought the obvious solution would be to use `np.asarray`
to convert the values to `builtins.bool`, but this did not work:

```
>>> import numpy as np
>>> import builtins
>>> arr = np.array([True, False], dtype=np.bool)
>>> arr
array([ True, False])
>>> type(arr[0])
<class 'numpy.bool'>
>>> newarr = np.asarray(arr, dtype=builtins.bool)
>>> newarr
array([ True, False])
>>> type(newarr[0])
<class 'numpy.bool'>
```

They still end up being NumPy bools. Some other variations on this
approach all failed to produce proper Python Booleans. In the end,
what worked was to use `map()` to apply `builtins.bool` to every value
in the incoming arrays. This may not be as efficient as possible; a
possible optimization for the future is to look for a more efficient
way to cast the types, or avoid having to do it at all.

Author: mhucka

cirq-google/cirq_google/serialization/arg_func_langs.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import builtins
 import math
 import numbers
 from typing import cast, Dict, FrozenSet, Iterable, Iterator, List, Optional, Sequence, Union
@@ -474,9 +475,9 @@ def clifford_tableau_arg_to_proto(
     msg = v2.program_pb2.CliffordTableau() if out is None else out
     msg.num_qubits = value.n
     msg.initial_state = value.initial_state
-    msg.xs.extend(value.xs.flatten())
-    msg.rs.extend(value.rs.flatten())
-    msg.zs.extend(value.zs.flatten())
+    msg.xs.extend(map(builtins.bool, value.xs.flatten()))
+    msg.rs.extend(map(builtins.bool, value.rs.flatten()))
+    msg.zs.extend(map(builtins.bool, value.zs.flatten()))
     return msg