Spell-check the repository

doc/extending/creating_a_c_op.rst

@@ -930,7 +930,7 @@ discussed below.
 For every input which has a :attr:`dtype` attribute (this means
 Tensors), the following macros will be
 defined unless your `Op` class has an :attr:`Op.check_input` attribute
-defined to False. In these descrptions 'i' refers to the position
+defined to False. In these descriptions 'i' refers to the position
 (indexed from 0) in the input array.
 
 *       ``DTYPE_INPUT_{i}`` : NumPy dtype of the data in the array.

doc/extending/creating_an_op.rst

@@ -20,7 +20,7 @@ As an illustration, this tutorial will demonstrate how a simple Python-based
 
 .. note::
 
-    This is an introductury tutorial and as such it does not cover how to make
+    This is an introductory tutorial and as such it does not cover how to make
     an :class:`Op` that returns a view or modifies the values in its inputs. Thus, all
     :class:`Op`\s created with the instructions described here MUST return newly
     allocated memory or reuse the memory provided in the parameter
@@ -203,7 +203,7 @@ or :meth:`Op.make_thunk`.
 
 There are other methods that can be optionally defined by the :class:`Op`:
 
-  :meth:`Op.__eq__` and :meth:`Op.__hash__` define respectivelly equality
+  :meth:`Op.__eq__` and :meth:`Op.__hash__` define respectively equality
   between two :class:`Op`\s and the hash of an :class:`Op` instance.
   They will be used during the rewriting phase to merge nodes that are doing
   equivalent computations (same inputs, same operation).

doc/extending/scan.rst

@@ -92,7 +92,7 @@ designated **inner inputs** and **inner outputs**, respectively.
 ================
 
 The following are the different types of variables that `Scan` has the
-capacity to handle, along with their various caracteristics.
+capacity to handle, along with their various characteristics.
 
 **Sequence** : A sequence is an PyTensor variable which `Scan` will iterate
 over and give sub-elements to its inner function as input. A sequence

doc/library/tensor/random/utils.rst

@@ -12,7 +12,7 @@
 Guide
 =====
 
-PyTensor assignes NumPy RNG states (e.g. `Generator` or `RandomState` objects) to
+PyTensor assigns NumPy RNG states (e.g. `Generator` or `RandomState` objects) to
 each `RandomVariable`.  The combination of an RNG state, a specific
 `RandomVariable` type (e.g. `NormalRV`), and a set of distribution parameters
 uniquely defines the `RandomVariable` instances in a graph.

doc/scripts/docgen.py

@@ -21,7 +21,7 @@ if __name__ == '__main__':
         print('  --cache: use the doctree cache')
         print('  --rst: only compile the doc (requires sphinx)')
         print('  --nopdf: do not produce a PDF file from the doc, only HTML')
-        print('  --test: run all the code samples in the documentaton')
+        print('  --test: run all the code samples in the documentation')
         print('  --check: treat warnings as errors')
         print('  --help: this help')
         print('If one or more files are specified after the options then only '

doc/tutorial/shape_info.rst

@@ -44,7 +44,7 @@ You can create variables with static shape information as follows:
     pytensor.tensor.tensor("float64", shape=(4, 3, 2))
 
 
-You can also pass shape infomation directly to some :class:`Op`\s, like ``RandomVariables``
+You can also pass shape information directly to some :class:`Op`\s, like ``RandomVariables``
 
 .. code-block:: python
 

pytensor/compile/function/types.py

@@ -599,7 +599,7 @@ class Function:
         # helper function
         def checkSV(sv_ori, sv_rpl):
             """
-            Assert two SharedVariable follow some restirctions:
+            Assert two SharedVariable follow some restrictions:
                 1. same type
                 2. same shape or dim?
             """

pytensor/compile/profiling.py

@@ -165,7 +165,7 @@ def print_global_stats():
         print(
             (
                 "Global stats: ",
-                f"Time elasped since PyTensor import = {time.perf_counter() - pytensor_imported_time:6.3f}s, "
+                f"Time elapsed since PyTensor import = {time.perf_counter() - pytensor_imported_time:6.3f}s, "
                 f"Time spent in PyTensor functions = {total_fct_exec_time:6.3f}s, "
                 "Time spent compiling PyTensor functions: "
                 f"rewriting = {total_graph_rewrite_time:6.3f}s, linking = {total_time_linker:6.3f}s ",
@@ -768,7 +768,7 @@ class ProfileStats:
                     f"    output {int(idx)}: dtype={dtype}, shape={sh}, strides={st}{off}",
                     file=file,
                 )
-            # Same as before, this I've sacrificied some information making
+            # Same as before, this I've sacrificed some information making
             # the output more readable
         print(
             "   ... (remaining %i Apply instances account for "

pytensor/configdefaults.py

@@ -792,7 +792,7 @@ def add_testvalue_and_checking_configvars():
         "print_test_value",
         (
             "If 'True', the __eval__ of an PyTensor variable will return its test_value "
-            "when this is available. This has the practical conseguence that, e.g., "
+            "when this is available. This has the practical consequence that, e.g., "
             "in debugging `my_var` will print the same as `my_var.tag.test_value` "
             "when a test value is defined."
         ),
@@ -1099,7 +1099,7 @@ def add_optimizer_configvars():
 
     config.add(
         "optdb__position_cutoff",
-        "Where to stop eariler during optimization. It represent the"
+        "Where to stop earlier during optimization. It represent the"
         " position of the optimizer where to stop.",
         FloatParam(np.inf),
         in_c_key=False,

pytensor/graph/replace.py

@@ -103,7 +103,7 @@ def graph_replace(
     # inputs do not have owners
     # this is exactly the reason to clone conditions
     equiv = {c: c.clone(name=f"i-{i}") for i, c in enumerate(conditions)}
-    # some replace keys may dissapear
+    # some replace keys may disappear
     # the reason is they are outside the graph
     # clone the graph but preserve the equiv mapping
     fg = FunctionGraph(

pytensor/graph/rewriting/db.py

@@ -198,7 +198,7 @@ class RewriteDatabaseQuery:
         Parameters
         ==========
         include:
-            A set of tags such that every rewirte obtained through this
+            A set of tags such that every rewrite obtained through this
             `RewriteDatabaseQuery` must have **one** of the tags listed. This
             field is required and basically acts as a starting point for the
             search.

pytensor/link/numba/dispatch/elemwise_codegen.py

@@ -81,7 +81,7 @@ def make_outputs(
         dtype = numba.from_dtype(np.dtype(dtype))
         arrtype = types.Array(dtype, len(iter_shape), "C")
         ar_types.append(arrtype)
-        # This is actually an interal numba function, I guess we could
+        # This is actually an internal numba function, I guess we could
         # call `numba.nd.unsafe.ndarray` instead?
         shape = [
             length if not bc_dim else one for length, bc_dim in zip(iter_shape, bc)

pytensor/link/numba/dispatch/scalar.py

@@ -60,7 +60,7 @@ def numba_funcify_ScalarOp(op, node, **kwargs):
     input_inner_dtypes = None
     output_inner_dtype = None
 
-    # Cython functions might have an additonal argument
+    # Cython functions might have an additional argument
     has_pyx_skip_dispatch = False
 
     if scalar_func_path.startswith("scipy.special"):

pytensor/link/numba/dispatch/scan.py

@@ -57,7 +57,7 @@ def numba_funcify_Scan(op, node, **kwargs):
     # Apply inner rewrites
     # TODO: Not sure this is the right place to do this, should we have a rewrite that
     #  explicitly triggers the optimization of the inner graphs of Scan?
-    #  The C-code deffers it to the make_thunk phase
+    #  The C-code defers it to the make_thunk phase
     rewriter = op.mode_instance.optimizer
     rewriter(op.fgraph)
 

pytensor/misc/ordered_set.py

@@ -6,7 +6,7 @@ from collections.abc import MutableSet
 def check_deterministic(iterable):
     # Most places where OrderedSet is used, pytensor interprets any exception
     # whatsoever as a problem that an optimization introduced into the graph.
-    # If I raise a TypeError when the DestoryHandler tries to do something
+    # If I raise a TypeError when the DestroyHandler tries to do something
     # non-deterministic, it will just result in optimizations getting ignored.
     # So I must use an assert here. In the long term we should fix the rest of
     # pytensor to use exceptions correctly, so that this can be a TypeError.

pytensor/misc/pkl_utils.py

@@ -268,7 +268,7 @@ def load(f, persistent_load=PersistentNdarrayLoad):
     :type f: file
 
     :param persistent_load: The persistent loading function to use for
-        unpickling. This must be compatible with the `persisten_id` function
+        unpickling. This must be compatible with the `persistent_id` function
         used when pickling.
     :type persistent_load: callable, optional
 

pytensor/scan/op.py

@@ -110,7 +110,7 @@ err_msg1 = (
     "that scan uses in each of its iterations. "
     "In order to solve this issue if the two variable currently "
     "have the same dimensionality, you can increase the "
-    "dimensionality of the varialbe in the initial state of scan "
+    "dimensionality of the variable in the initial state of scan "
     "by using dimshuffle or shape_padleft. "
 )
 err_msg2 = (
@@ -138,7 +138,7 @@ err_msg3 = (
     "The first dimension of this "
     "matrix corresponds to the number of previous time-steps "
     "that scan uses in each of its iterations. "
-    "In order to solve this issue if the two varialbe currently "
+    "In order to solve this issue if the two variable currently "
     "have the same dimensionality, you can increase the "
     "dimensionality of the variable in the initial state of scan "
     "by using dimshuffle or shape_padleft. "

pytensor/tensor/basic.py

@@ -647,7 +647,7 @@ def _conversion(real_value: Op, name: str) -> Op:
     return real_value
 
 
-# These _conver_to_<type> functions have leading underscores to indicate that
+# These _convert_to_<type> functions have leading underscores to indicate that
 # they should not be called directly.  They do not perform sanity checks about
 # what types you are casting to what.  That logic is implemented by the
 # `cast()` function below.
@@ -3844,7 +3844,7 @@ class AllocEmpty(COp):
         # False and it is set to true only in DebugMode.
         # We can't set it in the type as other make_node can reuse the type.
         # We can't set it in the variable as it isn't copied when we copy
-        # the variale. So we set it in the tag.
+        # the variable. So we set it in the tag.
         output.tag.nan_guard_mode_check = False
         return Apply(self, _shape, [output])
 

pytensor/tensor/rewriting/basic.py

@@ -721,7 +721,7 @@ def local_alloc_unary(fgraph, node):
 def local_cast_cast(fgraph, node):
     """cast(cast(x, dtype1), dtype2)
 
-    when those contrain:
+    when those constrain:
     dtype1 == dtype2
     OR the base dtype is the same (int, uint, float, complex)
           and the first cast cause an upcast.

pytensor/tensor/subtensor.py

@@ -1738,7 +1738,7 @@ class IncSubtensor(COp):
         different types of arrays.
 
         """
-        # Parameters of PyArrary_FromAny are:
+        # Parameters of PyArray_FromAny are:
         # array
         # dtype: we pass NULL to say any dtype is acceptable, so the existing
         #        dtype will be copied
@@ -2200,7 +2200,7 @@ class AdvancedIncSubtensor1(COp):
         different types of arrays.
 
         """
-        # Parameters of PyArrary_FromAny are:
+        # Parameters of PyArray_FromAny are:
         # array
         # dtype: we pass NULL to say any dtype is acceptable, so the existing
         #        dtype will be copied

tests/tensor/random/test_op.py

@@ -110,7 +110,7 @@ def test_RandomVariable_basics():
     rv_shape = rv._infer_shape(at.constant([]), (), [])
     assert rv_shape.equals(at.constant([], dtype="int64"))
 
-    # Integer-specificed `dtype`
+    # Integer-specified `dtype`
     dtype_1 = all_dtypes[1]
     rv_node = rv.make_node(None, None, 1)
     rv_out = rv_node.outputs[1]

tests/tensor/rewriting/test_math.py

@@ -132,9 +132,9 @@ rewrite_mode = get_mode(rewrite_mode)
 
 dimshuffle_lift = out2in(local_dimshuffle_lift)
 
-_stablize_rewrites = RewriteDatabaseQuery(include=["fast_run"])
-_stablize_rewrites.position_cutoff = 1.51
-_stablize_rewrites = optdb.query(_stablize_rewrites)
+_stabilize_rewrites = RewriteDatabaseQuery(include=["fast_run"])
+_stabilize_rewrites.position_cutoff = 1.51
+_stabilize_rewrites = optdb.query(_stabilize_rewrites)
 
 _specialize_rewrites = RewriteDatabaseQuery(include=["fast_run"])
 _specialize_rewrites.position_cutoff = 2.01
@@ -154,7 +154,7 @@ def rewrite(g, level="fast_run"):
     elif level == "specialize":
         _specialize_rewrites.rewrite(g)
     elif level == "stabilize":
-        _stablize_rewrites.rewrite(g)
+        _stabilize_rewrites.rewrite(g)
     else:
         raise ValueError(level)
     return g
@@ -2989,7 +2989,7 @@ class TestLocalErfc:
 
         # TODO: fix this problem: The python code upcast somewhere internally
         #  some value of float32 to python float for part of its computation.
-        #  That makes the c and python code generate sligtly different values
+        #  That makes the c and python code generate slightly different values
         if not (
             config.floatX == "float32" and config.mode in ["DebugMode", "DEBUG_MODE"]
         ):

tests/tensor/rewriting/test_subtensor.py

@@ -424,7 +424,7 @@ def test_local_subtensor_remove_broadcastable_index():
     # testing local_subtensor_remove_broadcastable_index optimization
     #
     # tests removing broadcastable dimensions with index 0 or -1,
-    # otherwise the optimzation should not be applied
+    # otherwise the optimization should not be applied
 
     mode = get_default_mode()
     mode = mode.including("local_subtensor_remove_broadcastable_index")
@@ -433,7 +433,7 @@ def test_local_subtensor_remove_broadcastable_index():
     y2 = x.dimshuffle("x", 1, 0, "x")
     y3 = x.dimshuffle("x", 1, "x", 0, "x")
 
-    # testing for cases that the optimzation should be applied
+    # testing for cases that the optimization should be applied
     z1 = y1[:, 0, :]
     z2 = y1[:, -1, :]
     z3 = y2[0, :, :, -1]
@@ -459,7 +459,7 @@ def test_local_subtensor_remove_broadcastable_index():
     xn = rng.random((5, 5))
     f(xn)
 
-    # testing for cases that the optimzation should not be applied
+    # testing for cases that the optimization should not be applied
     # to verify that other subtensor usage are passed without errors
     w1 = y1[3, 0, :]
     w2 = y1[2:4, -1, :]

tests/tensor/test_subtensor.py

@@ -1445,11 +1445,11 @@ class TestIncSubtensor:
         for do_set in [False, True]:
 
             if do_set:
-                resut = set_subtensor(a[sl1, sl2], increment)
+                result = set_subtensor(a[sl1, sl2], increment)
             else:
-                resut = inc_subtensor(a[sl1, sl2], increment)
+                result = inc_subtensor(a[sl1, sl2], increment)
 
-            f = pytensor.function([a, increment, sl2_end], resut)
+            f = pytensor.function([a, increment, sl2_end], result)
 
             val_a = np.ones((5, 5))
             val_inc = 2.3
@@ -1517,8 +1517,8 @@ class TestIncSubtensor:
 
         for method in [set_subtensor, inc_subtensor]:
 
-            resut = method(a[sl1, sl3, sl2], increment)
-            f = pytensor.function([a, increment, sl2_end], resut)
+            result = method(a[sl1, sl3, sl2], increment)
+            f = pytensor.function([a, increment, sl2_end], result)
 
             expected_result = np.copy(val_a)
             result = f(val_a, val_inc, val_sl2_end)
@@ -1531,9 +1531,9 @@ class TestIncSubtensor:
             utt.assert_allclose(result, expected_result)
 
             # Test when we broadcast the result
-            resut = method(a[sl1, sl2], increment)
+            result = method(a[sl1, sl2], increment)
 
-            f = pytensor.function([a, increment, sl2_end], resut)
+            f = pytensor.function([a, increment, sl2_end], result)
 
             expected_result = np.copy(val_a)
             result = f(val_a, val_inc, val_sl2_end)