Spell-check the repository

Author: Armavica

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 @@
         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 @@ def copy(
         # 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 @@ def summary_nodes(self, file=sys.stderr, N=None):
                     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(