Replace not `... == ...` with `... != ...`

aesara/compile/function/types.py

@@ -1128,7 +1128,7 @@ def _pickle_Function(f):
     # HACK to detect aliased storage.
     # This is here because aliased relationships are not [currently]
     # preserved across the pickle operation
-    if not (f.pickle_aliased_memory_strategy == "ignore"):
+    if f.pickle_aliased_memory_strategy != "ignore":
         all_data = input_storage + inputs_data
         for i, d_i in enumerate(all_data):
             for j, d_j in enumerate(all_data):

aesara/compile/io.py

@@ -79,7 +79,7 @@ class SymbolicInput:
             raise TypeError(f"name must be a string! (got: {self.name})")
         self.update = update
         if update is not None:
-            if not variable.type == update.type:
+            if variable.type != update.type:
                 raise TypeError(
                     f"Variable '{variable}' has type {variable.type} but an update of "
                     f"type {update.type}. The type of the update should be "

aesara/configdefaults.py

@@ -1240,7 +1240,7 @@ def add_multiprocessing_configvars():
                 f"The environment variable OMP_NUM_THREADS should be a number, got '{var}'."
             )
         else:
-            default_openmp = not int(var) == 1
+            default_openmp = int(var) != 1
     else:
         # Check the number of cores availables.
         count = os.cpu_count()

aesara/d3viz/d3viz.py

@@ -82,7 +82,7 @@ def d3viz(fct, outfile, copy_deps=True, *args, **kwargs):
 
     # Create output directory if not existing
     outdir = os.path.dirname(outfile)
-    if not outdir == "" and not os.path.exists(outdir):
+    if outdir != "" and not os.path.exists(outdir):
         os.makedirs(outdir)
 
     # Read template HTML file

aesara/gpuarray/dnn.py

@@ -248,7 +248,7 @@ def dnn_available(context_name):
 
     ctx = get_context(context_name)
 
-    if not ctx.kind == b"cuda":
+    if ctx.kind != b"cuda":
         dnn_available.msg = "Not on a CUDA device."
         return False
 
@@ -719,7 +719,7 @@ def ensure_dt(val, default, name, dtype):
         val = as_scalar(val)
     if not isinstance(val.type, aesara.scalar.Scalar):
         raise TypeError(f"{name}: expected a scalar value")
-    if not val.type.dtype == dtype:
+    if val.type.dtype != dtype:
         val = val.astype(dtype)
     return val
 

aesara/gpuarray/pool.py

@@ -258,7 +258,7 @@ class GpuAveragePoolGrad(CGpuKernelBase):
         if pad is None:
             pad = (0,) * nd
         elif isinstance(pad, (tuple, list)):
-            if max(pad) != 0 and not self.mode == "average_exc_pad":
+            if max(pad) != 0 and self.mode != "average_exc_pad":
                 raise ValueError("Padding must be zero for average_exc_pad")
         ws = as_tensor_variable(ws)
         stride = as_tensor_variable(stride)

aesara/graph/basic.py

@@ -249,7 +249,7 @@ class Apply(Node):
         remake_node = False
         new_inputs = inputs[:]
         for i, (curr, new) in enumerate(zip(self.inputs, new_inputs)):
-            if not curr.type == new.type:
+            if curr.type != new.type:
                 if strict:
                     # If compatible, casts new into curr.type
                     new_inputs[i] = curr.type.filter_variable(new)

aesara/graph/fg.py

@@ -450,7 +450,7 @@ class FunctionGraph(MetaObject):
         # TODO: ERROR HANDLING FOR LISTENERS (should it complete the change or revert it?)
         if node == "output":
             r = self.outputs[i]
-            if not r.type == new_var.type:
+            if r.type != new_var.type:
                 raise TypeError(
                     f"The type of the replacement ({new_var.type}) must be the"
                     f" same as the type of the original Variable ({r.type})."
@@ -458,7 +458,7 @@ class FunctionGraph(MetaObject):
             self.outputs[i] = new_var
         else:
             r = node.inputs[i]
-            if not r.type == new_var.type:
+            if r.type != new_var.type:
                 raise TypeError(
                     f"The type of the replacement ({new_var.type}) must be the"
                     f" same as the type of the original Variable ({r.type})."

aesara/graph/opt.py

@@ -2832,7 +2832,7 @@ def _check_chain(r, chain):
         elif r.owner is None:
             return False
         elif isinstance(elem, Op):
-            if not r.owner.op == elem:
+            if r.owner.op != elem:
                 return False
         else:
             try:

aesara/link/basic.py

@@ -565,7 +565,7 @@ class WrapLinker(Linker):
 
         order_list0 = order_lists[0]
         for order_list in order_lists[1:]:
-            if not order_list0 == order_list:
+            if order_list0 != order_list:
                 raise Exception(
                     "All linkers to WrapLinker should execute operations in the same order."
                 )

aesara/scan/basic.py

@@ -761,7 +761,7 @@ def scan(
             if isinstance(inner_out.type, TensorType) and return_steps.get(pos, 0) != 1:
                 outputs[pos] = at.unbroadcast(shape_padleft(inner_out), 0)
 
-        if return_list is not True and len(outputs) == 1:
+        if not return_list and len(outputs) == 1:
             outputs = outputs[0]
 
         return (outputs, updates)
@@ -1136,7 +1136,7 @@ def scan(
             # refers to update rule of index -1 - `pos`.
             update_map[sit_sot_shared[abs(pos) - 1]] = _scan_out_list[idx][-1]
     scan_out_list = [x for x in scan_out_list if x is not None]
-    if return_list is not True and len(scan_out_list) == 1:
+    if not return_list and len(scan_out_list) == 1:
         scan_out_list = scan_out_list[0]
     elif len(scan_out_list) == 0:
         scan_out_list = None

aesara/tensor/basic_opt.py

@@ -1390,7 +1390,7 @@ class ShapeFeature(features.Feature):
                 return False
             opx = dx.owner.op
             opy = dy.owner.op
-            if not (opx.i == opy.i):
+            if opx.i != opy.i:
                 return False
             # FB I'm not sure if this handle correctly constants.
             if dx.owner.inputs[0] == dy.owner.inputs[0]:
@@ -1633,7 +1633,7 @@ def local_fill_sink(fgraph, node):
         if (
             hasattr(client, "op")
             and isinstance(client.op, Elemwise)
-            and not client.op == fill
+            and client.op != fill
         ):
             client_inputs = client.inputs[:]
             client_inputs[cl_idx] = c

aesara/tensor/elemwise.py

@@ -187,7 +187,7 @@ class DimShuffle(ExternalCOp):
     def make_node(self, _input):
         input = as_tensor_variable(_input)
         ib = tuple(input.type.broadcastable)
-        if not ib == self.input_broadcastable:
+        if ib != self.input_broadcastable:
             if len(ib) != len(self.input_broadcastable):
                 raise TypeError(
                     "The number of dimensions of the "

aesara/tensor/math_opt.py

@@ -1021,7 +1021,7 @@ class AlgebraicCanonizer(LocalOptimizer):
 
         assert (new.type == out.type) == (not (new.type != out.type))
 
-        if not (new.type == out.type):
+        if new.type != out.type:
             new = fill_chain(new, node.inputs)[0]
 
         if new.type == out.type:
@@ -1138,7 +1138,7 @@ def local_sum_prod_mul_by_scalar(fgraph, node):
                 new_op_input_nb_elements = new_op_input.size
                 new_op_output = node.op(new_op_input)
 
-            if not len(non_scalars) == 0:
+            if len(non_scalars) != 0:
                 # Copy over stacktrace from previous output to new mul op,
                 # for same reason as above.
                 copy_stack_trace(node.outputs, new_op_output)
@@ -2519,7 +2519,7 @@ def local_greedy_distributor(fgraph, node):
 
     rval = local_mul_canonizer.merge_num_denum(new_num, new_denum)
 
-    if not (rval.type == out.type):
+    if rval.type != out.type:
         # WHY DOES THIS HAPPEN?
         return False
 

aesara/tensor/nnet/abstract_conv.py

@@ -3300,7 +3300,7 @@ class AbstractConv_gradInputs(BaseAbstractConv):
         expected_topgrad_shape = get_conv_output_shape(
             imshp, kern.shape, self.border_mode, self.subsample, self.filter_dilation
         )
-        if not tuple(expected_topgrad_shape) == tuple(topgrad.shape):
+        if tuple(expected_topgrad_shape) != tuple(topgrad.shape):
             raise ValueError(
                 "invalid input_shape for gradInputs: the given input_shape "
                 "would produce an output of shape {}, but the given topgrad "

aesara/tensor/nnet/basic.py

@@ -2527,7 +2527,7 @@ class Prepend_scalar_constant_to_each_row(Op):
     def make_node(self, mat):
         # check type of input
         x = at.as_tensor_variable(mat)
-        if not mat.type.broadcastable == (False, False):
+        if mat.type.broadcastable != (False, False):
             raise TypeError("Expected a matrix as input")
         y = at.as_tensor_variable(self.val)
         assert y.ndim == 0
@@ -2574,7 +2574,7 @@ class Prepend_scalar_to_each_row(Op):
         x = at.as_tensor_variable(mat)
         if isinstance(val, float):
             val = aes.constant(val)
-        if not mat.type.broadcastable == (False, False):
+        if mat.type.broadcastable != (False, False):
             raise TypeError("Expected a matrix as input")
         y = at.as_tensor_variable(val)
         assert y.ndim == 0

aesara/tensor/nnet/conv.py

@@ -1085,7 +1085,7 @@ class ConvOp(OpenMPOp):
 
         # Determine gradient on inputs ########
         mode = "valid"
-        if not self.out_mode == "full":
+        if self.out_mode != "full":
             mode = "full"
 
         filters = kerns.dimshuffle((1, 0, 2, 3))
@@ -1416,7 +1416,7 @@ if(%(value)s != %(expected)s){
             d["self_imshp_logical_stride_c"] = int(
                 np.ceil(self.imshp_logical[2] / float(self.imshp[2]))
             )
-            if not self.imshp[0] == 1:
+            if self.imshp[0] != 1:
                 d["affectation"] = "+="
             d["all_shape"] = "1"
             d["dim_zz_const"] = "const"
@@ -1473,7 +1473,7 @@ if(kerns_dim[1] != img2d_dim[1]){
                 f"Type {node.inputs[0].type.dtype} not implemented"
             )
         d["gemm"] = "dgemm_"
-        if not d["type"] == "double":
+        if d["type"] != "double":
             d["gemm"] = "sgemm_"
 
         if self.imshp != self.imshp_logical or self.kshp != self.kshp_logical:

aesara/tensor/nnet/opt.py

@@ -448,7 +448,7 @@ def local_conv2d_gradinputs_cpu(fgraph, node):
             return None
 
     mode = "valid"
-    if not node.op.border_mode == "full":
+    if node.op.border_mode != "full":
         mode = "full"
     filters = kern.dimshuffle((1, 0, 2, 3))
     filters = filters[:, :, ::-1, ::-1]

aesara/tensor/shape.py

@@ -413,7 +413,7 @@ class SpecifyShape(COp):
             raise AssertionError(
                 f"SpecifyShape: Got {x.ndim} dimensions (shape {x.shape}), expected {ndim} dimensions with shape {tuple(shape)}."
             )
-        if not np.all(x.shape == shape):
+        if x.shape != tuple(shape):
             raise AssertionError(
                 f"SpecifyShape: Got shape {x.shape}, expected {tuple(shape)}."
             )

aesara/typed_list/type.py

@@ -75,7 +75,7 @@ class TypedListType(CType):
             return 0
 
     def values_eq(self, a, b):
-        if not len(a) == len(b):
+        if len(a) != len(b):
             return False
 
         for x in range(len(a)):

tests/graph/test_features.py

@@ -74,7 +74,7 @@ class TestNodeFinder:
 
         assert hasattr(g, "get_nodes")
         for type, num in ((add, 3), (sigmoid, 3), (dot, 2)):
-            if not len([t for t in g.get_nodes(type)]) == num:
+            if len([t for t in g.get_nodes(type)]) != num:
                 raise Exception("Expected: %i times %s" % (num, type))
         new_e0 = add(y, z)
         assert e0.owner in g.get_nodes(dot)
@@ -83,7 +83,7 @@ class TestNodeFinder:
         assert e0.owner not in g.get_nodes(dot)
         assert new_e0.owner in g.get_nodes(add)
         for type, num in ((add, 4), (sigmoid, 3), (dot, 1)):
-            if not len([t for t in g.get_nodes(type)]) == num:
+            if len([t for t in g.get_nodes(type)]) != num:
                 raise Exception("Expected: %i times %s" % (num, type))
 
 

tests/tensor/test_math_opt.py

@@ -1740,7 +1740,7 @@ class TestFusion:
             topo = f.maker.fgraph.toposort()
             topo_ = [n for n in topo if not isinstance(n.op, self.topo_exclude)]
             if assert_len_topo:
-                if not len(topo_) == nb_elemwise:
+                if len(topo_) != nb_elemwise:
                     fail3.append((id, topo_, nb_elemwise))
                 if nb_elemwise == 1:
                     # if no variable appears multiple times in the
@@ -1753,7 +1753,7 @@ class TestFusion:
                         )
                         assert expected_len_sym_inputs == len(sym_inputs)
 
-            if not out_dtype == out.dtype:
+            if out_dtype != out.dtype:
                 fail4.append((id, out_dtype, out.dtype))
 
         assert len(fail1 + fail2 + fail3 + fail4) == 0