Fix typos in tests

tests/compile/function/test_pfunc.py

@@ -710,7 +710,7 @@ class TestAliasingRules:
     # with the memory of normal python variables that the user uses.
     #
     # 2. shared variables are allocated in this memory space, as are the
-    # temporaries used for Function evalution.
+    # temporaries used for Function evaluation.
     #
     # 3. Physically, this managed memory space may be spread across the host,
     # on a GPU device(s), or even on a remote machine.

tests/compile/function/test_types.py

@@ -387,7 +387,7 @@ class TestFunction:
             )
             cpy = ori.copy(swap=swap)
 
-            # run fuction several time
+            # run function several times
             ori(1), cpy(1), cpy(2)
 
             # assert same SharedVariable are update in different function

tests/gpuarray/test_abstractconv.py

@@ -37,7 +37,7 @@ class TestDnnConv2d(BaseTestConv2d):
     def setup_class(cls):
         super().setup_class()
         cls.shared = staticmethod(gpuarray_shared_constructor)
-        # provide_shape is not used by the cuDNN impementation
+        # provide_shape is not used by the cuDNN implementation
         cls.provide_shape = [False]
 
     @pytest.mark.skipif(dnn_available(test_ctx_name), reason=dnn_available.msg)
@@ -131,7 +131,7 @@ class TestDnnConv3d(BaseTestConv3d):
     def setup_class(cls):
         super().setup_class()
         cls.shared = staticmethod(gpuarray_shared_constructor)
-        # provide_shape is not used by the cuDNN impementation
+        # provide_shape is not used by the cuDNN implementation
         cls.provide_shape = [False]
 
     @pytest.mark.skipif(dnn_available(test_ctx_name), reason=dnn_available.msg)

tests/gpuarray/test_basic_ops.py

@@ -206,7 +206,7 @@ def makeTester(
 
             for description, check in self.checks.items():
                 assert check(inputs, variables), (
-                    "Test %s::%s: Failed check: %s " "(inputs were %s, ouputs were %s)"
+                    "Test %s::%s: Failed check: %s " "(inputs were %s, outputs were %s)"
                 ) % (self.op, testname, description, inputs, variables)
 
     Checker.__name__ = name

tests/gpuarray/test_blocksparse.py

@@ -28,7 +28,7 @@ class TestBlockSparseGemvAndOuterGPUarray(TestBlockSparseGemvAndOuter):
     @pytest.mark.skip(
         reason="""
         This test is temporarily disabled since we disabled the output_merge
-        and alpha_merge optimizations for blocksparse due to brokeness.
+        and alpha_merge optimizations for blocksparse due to brokenness.
         Re-enable when those are re-added.
         """
     )

tests/gpuarray/test_dnn.py

@@ -1284,7 +1284,7 @@ def run_conv_small_batched_vs_multicall(inputs_shape, filters_shape, batch_sub):
     f = aesara.function([], [conv, sub_conv_top, sub_conv_bottom], mode=mode_with_gpu)
     res_all, res_batch_top, res_batch_bottom = f()
     for i in range(batch_sub):
-        # Check first ouputs.
+        # Check first outputs.
         utt.assert_allclose(res_batch_top[i], res_all[i])
         # Then check last outputs.
         p = batch_size - batch_sub + i

tests/gpuarray/test_misc.py

-# Test that normaly could be outside gpuarray, to have all gpuarray
+# Test that normally could be outside gpuarray, to have all gpuarray
 # tests in the same directory, we put them here.
 import numpy as np
 

tests/gpuarray/test_opt.py

@@ -291,7 +291,7 @@ def test_local_gpualloc_empty():
     topo = f.maker.fgraph.toposort()
     assert len(topo) == 1
     assert isinstance(topo[0].op, AllocEmpty)
-    # This return not initilized data, so we can only check the shape
+    # This return not initialized data, so we can only check the shape
     assert f(3).shape == (3,)
     assert _check_stack_trace(f)
 
@@ -302,7 +302,7 @@ def test_local_gpualloc_empty():
     topo = f.maker.fgraph.toposort()
     assert len(topo) == 3
     assert isinstance(topo[0].op, GpuAllocEmpty)
-    # This return not initilized data, so we can only check the shape
+    # This return not initialized data, so we can only check the shape
     assert f(3).shape == (3,)
     assert _check_stack_trace(f)
 
@@ -312,7 +312,7 @@ def test_local_gpualloc_empty():
     topo = f.maker.fgraph.toposort()
     assert len(topo) == 3
     assert isinstance(topo[0].op, GpuAllocEmpty)
-    # This return not initilized data, so we can only check the shape
+    # This return not initialized data, so we can only check the shape
     assert f(3, 4).shape == (3, 4)
     assert _check_stack_trace(f)
 
@@ -550,7 +550,7 @@ def test_local_gpu_elemwise():
     utt.assert_allclose(f(a_v, b_v, c_v), a_v + b_v + c_v)
     assert _check_stack_trace(f)
 
-    return  # Not yet implemeted
+    return  # Not yet implemented
     # Test multiple output
     a_s = aesara.scalar.float32()
     a = fmatrix()

tests/gpuarray/test_reduction.py

@@ -19,7 +19,7 @@ from tests.gpuarray.test_basic_ops import rand_gpuarray
 test_size = 10000
 
 # NB: This order of "unsorted axes" is arbitrary and is here
-# just to have the same informations on profile output
+# just to have the same information on profile output
 # from one test to another.
 unsorted_axes = (2, 4, 0, 3, 1)
 
@@ -88,7 +88,7 @@ class BaseTest:
             pytest.skip("No tensor ndim defined.")
         if self.tensor_size < 0 or self.tensor_size > 5:
             pytest.skip(
-                "We allow from 0 (included) to 5 (inclued) dimensons for these tests."
+                "We allow from 0 (included) to 5 (included) dimensons for these tests."
             )
         if self.shape is None:
             self.shape = self.get_shape()

tests/gpuarray/test_scan.py

@@ -549,7 +549,7 @@ class ScanGpuTests:
 
     def test_gpu_memory_usage(self):
         # This test validates that the memory usage of the defined aesara
-        # function is reasonnable when executed on the GPU. It checks for
+        # function is reasonable when executed on the GPU. It checks for
         # a bug in which one of scan's optimization was not applied which
         # made the scan node compute large and unnecessary outputs which
         # brought memory usage on the GPU to ~12G.

tests/graph/test_op.py

@@ -46,7 +46,7 @@ class MyType(Type):
     @staticmethod
     def may_share_memory(a, b):
         # As this represent a string and string are immutable, they
-        # never share memory in the DebugMode sence. This is needed as
+        # never share memory in the DebugMode sense. This is needed as
         # Python reuse string internally.
         return False
 

tests/graph/test_types.py

@@ -219,7 +219,7 @@ class TestEnumTypes:
             except AttributeError:
                 pass
             else:
-                raise Exception("EnumList with invalid name should faild.")
+                raise Exception("EnumList with invalid name should fail.")
 
             try:
                 EnumType(**{invalid_name: 0})

tests/link/test_vm.py

@@ -411,7 +411,7 @@ def test_reallocation():
     x = scalar("x")
     y = scalar("y")
     z = tanh(3 * x + y) + cosh(x + 5 * y)
-    # The functinality is currently implement for non lazy and non c VM only.
+    # The functionality is currently implement for non lazy and non c VM only.
     for linker in [
         VMLinker(allow_gc=False, lazy=False, use_cloop=False),
         VMLinker(allow_gc=True, lazy=False, use_cloop=False),

tests/scan/test_basic.py

@@ -872,7 +872,7 @@ class TestScan:
         # a bit of explaining:
         # due to the definition of sequences taps in scan, v_0[0] is
         # actually v_0[-2], and v_0[1] is v_0[-1]. The values v_0[2]
-        # and v_0[3] do not get uesd ( because you do not use v_0[t]
+        # and v_0[3] do not get used ( because you do not use v_0[t]
         # in scan) which might seem strange, but then again why not use
         # v_0[t] instead of v_0[t-2] in a real application ??
         # also vx0[0] corresponds to vx0[-2], vx0[1] to vx0[-1]
@@ -3267,7 +3267,7 @@ class TestScan:
 
     @pytest.mark.skip(
         reason="This test fails because not typed outputs_info "
-        "are always gived the smallest dtype. There is "
+        "are always given the smallest dtype. There is "
         "no upcast of outputs_info in scan for now.",
     )
     def test_outputs_info_not_typed(self):
@@ -3639,7 +3639,7 @@ class TestScan:
 
         grad(scan_outputs[0].sum(), out_init[1])
 
-        # Validate the connnection pattern is as it should be
+        # Validate the connection pattern is as it should be
         node = scan_outputs[0].owner
         connection_pattern = node.op.connection_pattern(node)
         expected_connection_pattern = [

tests/scan/test_opt.py

@@ -207,7 +207,7 @@ class TestPushOutScanOutputDot:
         scan_node = [
             node for node in f_opt.maker.fgraph.toposort() if isinstance(node.op, Scan)
         ][0]
-        # NOTE: WHEN INFER_SHAPE IS REENABLED, BELOW THE SCAN MUST
+        # NOTE: WHEN INFER_SHAPE IS RE-ENABLED, BELOW THE SCAN MUST
         # HAVE ONLY 1 OUTPUT.
         assert len(scan_node.op.outputs) == 2
         assert not isinstance(scan_node.op.outputs[0], Dot)
@@ -282,7 +282,7 @@ class TestPushOutSumOfDot:
         #
         # 'dim' has been reduced from 1000 to 5 to make the test run faster
 
-        # Parameters from an actual machine tranlation run
+        # Parameters from an actual machine translation run
         batch_size = 80
         seq_len = 50
         dim = 5

tests/sparse/test_basic.py

@@ -2728,7 +2728,7 @@ def _hv_switch(op, expected_function):
 
     :Parameters:
     - `op`: HStack or VStack class.
-    - `expected_function`: function from scipy for comparaison.
+    - `expected_function`: function from scipy for comparison.
     """
 
     class TestXStack(_TestHVStack):

tests/sparse/test_utils.py

@@ -9,7 +9,7 @@ from tests.sparse.test_basic import as_sparse_format
 
 
 def test_hash_from_sparse():
-    hashs = []
+    hashes = []
     rng = np.random.rand(5, 5)
 
     for format in ["csc", "csr"]:
@@ -41,11 +41,11 @@ def test_hash_from_sparse():
         ]:
             data = as_sparse_format(data, format)
 
-            hashs.append(hash_from_sparse(data))
+            hashes.append(hash_from_sparse(data))
 
         # test that different type of views and their copy give the same hash
         assert hash_from_sparse(rng[1:]) == hash_from_sparse(rng[1:].copy())
         assert hash_from_sparse(rng[1:3]) == hash_from_sparse(rng[1:3].copy())
         assert hash_from_sparse(rng[:4]) == hash_from_sparse(rng[:4].copy())
 
-    assert len(set(hashs)) == len(hashs)
+    assert len(set(hashes)) == len(hashes)

tests/tensor/nnet/test_blocksparse.py

@@ -174,7 +174,7 @@ class TestBlockSparseGemvAndOuter(utt.InferShapeTester):
         utt.assert_allclose(ref_out, th_out)
 
     def test_sparseblockgemvF(self):
-        # Test the fortan order for W (which can happen in the grad for some
+        # Test the fortran order for W (which can happen in the grad for some
         # graphs).
 
         b = fmatrix()
@@ -255,7 +255,7 @@ class TestBlockSparseGemvAndOuter(utt.InferShapeTester):
 
         W_val, h_val, iIdx_val, b_val, oIdx_val = self.gemv_data()
 
-        # just make sure that it runs correcly and all the shapes are ok.
+        # just make sure that it runs correctly and all the shapes are ok.
         b_g, W_g, h_g = f(W_val, h_val, iIdx_val, b_val, oIdx_val)
 
         assert b_g.shape == b_val.shape

tests/tensor/nnet/test_conv.py

@@ -408,7 +408,7 @@ class TestConv2D(utt.InferShapeTester):
 
     @pytest.mark.slow
     def test_invalid_input_shape(self):
-        # Tests that when the shape gived at build time is not the same as
+        # Tests that when the shape given at build time is not the same as
         # run time we raise an error
 
         for unroll_batch in [None, 1, 3]:

tests/tensor/nnet/test_neighbours.py

@@ -233,7 +233,7 @@ class TestImages2Neibs(unittest_tools.InferShapeTester):
             [13, 14, 10, 18, 19, 15, 23, 24, 20],
         ]
 
-        # TODO test discontinous image
+        # TODO test discontinuous image
 
         for shp_idx, (shape, neib_shape, neib_step, expected) in enumerate(
             [

tests/tensor/test_basic.py

@@ -2136,7 +2136,7 @@ def test_tile():
 
         # error raising test:
         # reps is vector and len(reps_value) > ndim,
-        # reps_value is the real value when excuting the function.
+        # reps_value is the real value when executing the function.
         reps = ivector()
         r = [2, 3, 4, 5, 6, 7]
         reps_ = r[: k + 2]

tests/tensor/test_gc.py

@@ -53,7 +53,7 @@ def test_gc_never_pickles_temporaries():
         # len_pre_g = len(pre_g)
 
         # We can't compare the content or the length of the string
-        # between f and g. 2 reason, we store some timming information
+        # between f and g. 2 reason, we store some timing information
         # in float. They won't be the same each time. Different float
         # can have different length when printed.
 

tests/tensor/test_math_scipy.py

@@ -640,7 +640,7 @@ class TestSoftplus:
         utt.verify_grad(aet.softplus, [np.random.rand(3, 4)])
 
     def test_accuracy(self):
-        # Test all aproximations are working (cutoff points are -37, 18, 33.3)
+        # Test all approximations are working (cutoff points are -37, 18, 33.3)
         x_test = np.array([-40.0, -17.5, 17.5, 18.5, 40.0])
         y_th = aet.softplus(x_test).eval()
         y_np = np.log1p(np.exp(x_test))

tests/tensor/test_mlp.py

@@ -164,7 +164,7 @@ class HiddenLayer:
         """
         self.input = input
 
-        # `W` is initialized with `W_values` which is uniformely sampled
+        # `W` is initialized with `W_values` which is uniformly sampled
         # from -6./sqrt(n_in+n_hidden) and 6./sqrt(n_in+n_hidden)
         # the output of uniform if converted using asarray to dtype
         # aesara.config.floatX so that the code is runable on GPU
@@ -188,7 +188,7 @@ class MLP:
 
     A multilayer perceptron is a feedforward artificial neural network model
     that has one layer or more of hidden units and nonlinear activations.
-    Intermidiate layers usually have as activation function thanh or the
+    Intermediate layers usually have as activation function thanh or the
     sigmoid function (defined here by a ``SigmoidalLayer`` class)  while the
     top layer is a softamx layer (defined here by a ``LogisticRegression``
     class).

tests/tensor/test_nlinalg.py

@@ -52,7 +52,7 @@ def test_pseudoinverse_correctness():
     assert ri.shape[0] == r.shape[1]
     assert ri.shape[1] == r.shape[0]
     assert ri.dtype == r.dtype
-    # Note that pseudoinverse can be quite unprecise so I prefer to compare
+    # Note that pseudoinverse can be quite imprecise so I prefer to compare
     # the result with what np.linalg returns
     assert _allclose(ri, np.linalg.pinv(r))
 

tests/tensor/test_sharedvar.py

@@ -327,7 +327,7 @@ def makeSharedTester(
             if x.__class__.__name__ != "csr_matrix":
                 # sparse matrix don't support inplace affectation
                 nd += 1
-                # THIS DOENS'T DO WHAT WE EXPECT the content of a is
+                # THIS DOESN'T DO WHAT WE EXPECT the content of a is
                 # not updated for GpuArray, but it is for ndarray
                 x_shared.get_value(borrow=True)[:] = nd
                 assert may_share_memory(old_data, x_shared.container.storage[0])
@@ -347,7 +347,7 @@ def makeSharedTester(
             )
 
             # Test by set_value with borrow=False when new data cast.
-            # specificaly useful for gpu data
+            # specifically useful for gpu data
             nd += 1
             old_data = x_shared.container.storage[0]
             x_shared.set_value(self.cast_value(nd), borrow=False)

tests/tensor/test_slinalg.py

@@ -142,7 +142,7 @@ def test_eigvalsh():
         refw = scipy.linalg.eigvalsh(a, b)
         np.testing.assert_array_almost_equal(w, refw)
 
-    # We need to test None separatly, as otherwise DebugMode will
+    # We need to test None separately, as otherwise DebugMode will
     # complain, as this isn't a valid ndarray.
     b = None
     B = aet.NoneConst

tests/tensor/test_utils.py

@@ -8,7 +8,7 @@ from aesara.tensor.utils import hash_from_ndarray, shape_of_variables
 
 
 def test_hash_from_ndarray():
-    hashs = []
+    hashes = []
     rng = np.random.rand(5, 5)
 
     for data in [
@@ -37,9 +37,9 @@ def test_hash_from_ndarray():
         rng[::-1],
     ]:
         data = np.asarray(data)
-        hashs.append(hash_from_ndarray(data))
+        hashes.append(hash_from_ndarray(data))
 
-    assert len(set(hashs)) == len(hashs)
+    assert len(set(hashes)) == len(hashes)
 
     # test that different type of views and their copy give the same hash
     assert hash_from_ndarray(rng[1:]) == hash_from_ndarray(rng[1:].copy())

tests/test_gradient.py

@@ -901,7 +901,7 @@ class TestDisconnectedGrad:
         x = matrix("x")
 
         # This MUST raise a DisconnectedInputError error.
-        # This also rasies an additional warning from gradients.py.
+        # This also raises an additional warning from gradients.py.
         with pytest.raises(DisconnectedInputError):
             grad(disconnected_grad(x).sum(), x)
 
@@ -912,7 +912,7 @@ class TestDisconnectedGrad:
         b = matrix("b")
         y = a + disconnected_grad(b)
         # This MUST raise a DisconnectedInputError error.
-        # This also rasies an additional warning from gradients.py.
+        # This also raises an additional warning from gradients.py.
         with pytest.raises(DisconnectedInputError):
             grad(y.sum(), b)
 

tests/test_rop.py

@@ -61,7 +61,7 @@ break_op = BreakRop()
 
 class RopLopChecker:
     """
-    Don't peform any test, but provide the function to test the
+    Don't perform any test, but provide the function to test the
     Rop to class that inherit from it.
     """
 

tests/typed_list/test_type.py

@@ -111,7 +111,7 @@ class TestTypedListType:
         assert myNestedType2 != myNestedType3
 
     def test_nested_list_arg(self):
-        # test for the 'depth' optionnal argument
+        # test for the 'depth' optional argument
 
         myNestedType = TypedListType(
             TensorType(aesara.config.floatX, (False, False)), 3