remove bincount uint64 restriction, it was a left over from the numpy implementation

theano/tensor/extra_ops.py

@@ -504,18 +504,6 @@ def bincount(x, weights=None, minlength=None, assert_nonneg=False):
     .. versionadded:: 0.6
 
     """
-    compatible_type = ('int8', 'int16', 'int32', 'int64',
-                       'uint8', 'uint16', 'uint32')
-    unsupported_dtypes = ('uint64',)
-
-    if x.dtype in unsupported_dtypes:
-            raise TypeError(
-                ("Input dtype %s is not supported, "
-                 % unsupported_dtypes), x.dtype)
-
-    if x.dtype not in compatible_type:
-        raise TypeError("Inputs dtype must be an integer.")
-
     if x.ndim != 1:
         raise TypeError("Inputs must be of dimension 1.")
 

theano/tensor/tests/test_extra_ops.py

@@ -138,18 +138,6 @@ class TestBinCountOp(utt.InferShapeTester):
                       'uint8', 'uint16', 'uint32', 'uint64'):
             x = T.vector('x', dtype=dtype)
 
-            # uint64 always fails
-            # int64 and uint32 also fail if python int are 32-bit
-            int_bitwidth = theano.gof.python_int_bitwidth()
-            if int_bitwidth == 64:
-                numpy_unsupported_dtypes = ('uint64',)
-            if int_bitwidth == 32:
-                numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
-            # uint64 always fails
-            if dtype in numpy_unsupported_dtypes:
-                self.assertRaises(TypeError, bincount, x)
-
-            else:
             a = np.random.random_integers(50, size=(25)).astype(dtype)
             weights = np.random.random((25,)).astype(config.floatX)