Upgrade pre-commit tools and apply black format

Author: Armavica

.pre-commit-config.yaml

@@ -25,7 +25,7 @@ repos:
       - id: pyupgrade
         args: [--py38-plus]
   - repo: https://github.com/psf/black
-    rev: 22.10.0
+    rev: 23.1.0
     hooks:
       - id: black
         language_version: python3
@@ -52,7 +52,7 @@ repos:
           )$
         args: ['--in-place', '--remove-all-unused-imports', '--remove-unused-variable']
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.991
+    rev: v1.0.0
     hooks:
       - id: mypy
         language: python

pytensor/breakpoint.py

@@ -69,7 +69,6 @@ def __init__(self, name):
         self.name = name
 
     def make_node(self, condition, *monitored_vars):
-
         # Ensure that condition is an PyTensor tensor
         if not isinstance(condition, Variable):
             condition = as_tensor_variable(condition)
@@ -150,7 +149,6 @@ def infer_shape(self, fgraph, inputs, input_shapes):
         return input_shapes[1:]
 
     def connection_pattern(self, node):
-
         nb_inp = len(node.inputs)
         nb_out = nb_inp - 1
 

pytensor/compile/builders.py

@@ -900,7 +900,6 @@ def connection_pattern(self, node):
         return list(map(list, cpmat_self))
 
     def infer_shape(self, fgraph, node, shapes):
-
         # TODO: Use `fgraph.shape_feature` to do this instead.
         out_shapes = infer_shape(self.inner_outputs, self.inner_inputs, shapes)
 

pytensor/compile/debugmode.py

@@ -567,7 +567,6 @@ def _check_viewmap(fgraph, node, storage_map):
     """
 
     for oi, onode in enumerate(node.outputs):
-
         good_alias, bad_alias = {}, {}
         outstorage = storage_map[onode][0]
 
@@ -590,13 +589,11 @@ def _check_viewmap(fgraph, node, storage_map):
             if hasattr(inode.type, "may_share_memory") and inode.type.may_share_memory(
                 outstorage, in_storage
             ):
-
                 nodeid = id(inode)
                 bad_alias[nodeid] = ii
 
                 # check that the aliasing was declared in [view|destroy]_map
                 if [ii] == view_map.get(oi, None) or [ii] == destroy_map.get(oi, None):
-
                     good_alias[nodeid] = bad_alias.pop(nodeid)
 
         # TODO: make sure this is correct
@@ -1010,7 +1007,7 @@ def _check_preallocated_output(
                 aliased_inputs.add(r)
 
         _logger.debug("starting preallocated output checking")
-        for (name, out_map) in _get_preallocated_maps(
+        for name, out_map in _get_preallocated_maps(
             node,
             thunk,
             prealloc_modes,
@@ -1697,7 +1694,6 @@ def f():
                         sys.stdout.flush()
 
                     if thunk_c:
-
                         clobber = True
                         if thunk_py:
                             dmap = node.op.destroy_map

pytensor/compile/function/pfunc.py

@@ -176,7 +176,7 @@ def clone_inputs(i):
     # Fill update_d and update_expr with provided updates
     if updates is None:
         updates = []
-    for (store_into, update_val) in iter_over_pairs(updates):
+    for store_into, update_val in iter_over_pairs(updates):
         if not isinstance(store_into, SharedVariable):
             raise TypeError("update target must be a SharedVariable", store_into)
         if store_into in update_d:
@@ -471,7 +471,6 @@ def construct_pfunc_ins_and_outs(
             )
 
     if not fgraph:
-
         # Extend the outputs with the updates on input variables so they are
         # also cloned
         additional_outputs = [i.update for i in inputs if i.update]

pytensor/compile/function/types.py

@@ -596,6 +596,7 @@ def copy(
         pytensor.Function
             Copied pytensor.Function
         """
+
         # helper function
         def checkSV(sv_ori, sv_rpl):
             """
@@ -760,7 +761,6 @@ def checkSV(sv_ori, sv_rpl):
         for in_ori, in_cpy, ori, cpy in zip(
             maker.inputs, f_cpy.maker.inputs, self.input_storage, f_cpy.input_storage
         ):
-
             # Share immutable ShareVariable and constant input's storage
             swapped = swap is not None and in_ori.variable in swap
 
@@ -910,7 +910,6 @@ def restore_defaults():
                 if hasattr(i_var.type, "may_share_memory"):
                     is_aliased = False
                     for j in range(len(args_share_memory)):
-
                         group_j = zip(
                             [
                                 self.maker.inputs[k].variable
@@ -928,7 +927,6 @@ def restore_defaults():
                             )
                             for (var, val) in group_j
                         ):
-
                             is_aliased = True
                             args_share_memory[j].append(i)
                             break
@@ -1056,9 +1054,7 @@ def restore_defaults():
         elif self.unpack_single and len(outputs) == 1 and output_subset is None:
             return outputs[0]
         else:
-
             if self.output_keys is not None:
-
                 assert len(self.output_keys) == len(outputs)
 
                 if output_subset is None:
@@ -1399,7 +1395,6 @@ def prepare_fgraph(
         mode: "Mode",
         profile,
     ):
-
         rewriter = mode.optimizer
 
         try:
@@ -1422,7 +1417,6 @@ def prepare_fgraph(
                 # Add deep copy to respect the memory interface
                 insert_deepcopy(fgraph, inputs, outputs + additional_outputs)
         finally:
-
             # If the rewriter got interrupted
             if rewrite_time is None:
                 end_rewriter = time.perf_counter()
@@ -1598,7 +1592,6 @@ def create(self, input_storage=None, storage_map=None):
         for i, ((input, indices, subinputs), input_storage_i) in enumerate(
             zip(self.indices, input_storage)
         ):
-
             # Replace any default value given as a variable by its
             # container.  Note that this makes sense only in the
             # context of shared variables, but for now we avoid

pytensor/compile/profiling.py

@@ -65,7 +65,6 @@ def _atexit_print_fn():
             destination_file = config.profiling__destination
 
         with extended_open(destination_file, mode="w"):
-
             # Reverse sort in the order of compile+exec time
             for ps in sorted(
                 _atexit_print_list, key=lambda a: a.compile_time + a.fct_call_time
@@ -358,7 +357,7 @@ def class_impl(self):
         """
         # timing is stored by node, we compute timing by class on demand
         rval = {}
-        for (fgraph, node) in self.apply_callcount:
+        for fgraph, node in self.apply_callcount:
             typ = type(node.op)
             if self.apply_cimpl[node]:
                 impl = "C "
@@ -401,7 +400,7 @@ def compute_total_times(self):
 
         """
         rval = {}
-        for (fgraph, node) in self.apply_time:
+        for fgraph, node in self.apply_time:
             if node not in rval:
                 self.fill_node_total_time(fgraph, node, rval)
         return rval
@@ -437,7 +436,7 @@ def op_impl(self):
         """
         # timing is stored by node, we compute timing by Op on demand
         rval = {}
-        for (fgraph, node) in self.apply_callcount:
+        for fgraph, node in self.apply_callcount:
             if self.apply_cimpl[node]:
                 rval[node.op] = "C "
             else:
@@ -711,7 +710,7 @@ def summary_nodes(self, file=sys.stderr, N=None):
 
         atimes.sort(reverse=True, key=lambda t: (t[1], t[3]))
         tot = 0
-        for (f, t, a, nd_id, nb_call) in atimes[:N]:
+        for f, t, a, nd_id, nb_call in atimes[:N]:
             tot += t
             ftot = tot * 100 / local_time
             if nb_call == 0:
@@ -840,7 +839,7 @@ def summary_memory(self, file, N=None):
         var_mem = {}  # variable->size in bytes; don't include input variables
         node_mem = {}  # (fgraph, node)->total outputs size (only dense outputs)
 
-        for (fgraph, node) in self.apply_callcount:
+        for fgraph, node in self.apply_callcount:
             fct_memory.setdefault(fgraph, {})
             fct_memory[fgraph].setdefault(node, [])
             fct_shapes.setdefault(fgraph, {})
@@ -1610,7 +1609,7 @@ def exp_float32_op(op):
             printed_tip = True
 
         # tip 4
-        for (fgraph, a) in self.apply_time:
+        for fgraph, a in self.apply_time:
             node = a
             if isinstance(node.op, Dot) and all(
                 len(i.type.broadcastable) == 2 for i in node.inputs
@@ -1630,7 +1629,7 @@ def exp_float32_op(op):
         # The tip was about MRG_RandomStream which is removed
 
         # tip 6
-        for (fgraph, a) in self.apply_time:
+        for fgraph, a in self.apply_time:
             node = a
             if isinstance(node.op, Dot) and len({i.dtype for i in node.inputs}) != 1:
                 print(

pytensor/configdefaults.py

@@ -278,7 +278,6 @@ def short_platform(r=None, p=None):
 
 
 def add_basic_configvars():
-
     config.add(
         "floatX",
         "Default floating-point precision for python casts.\n"
@@ -388,7 +387,6 @@ def _is_greater_or_equal_0(x):
 
 
 def add_compile_configvars():
-
     config.add(
         "mode",
         "Default compilation mode",
@@ -631,7 +629,6 @@ def _is_valid_cmp_sloppy(v):
 
 
 def add_tensor_configvars():
-
     # This flag is used when we import PyTensor to initialize global variables.
     # So changing it after import will not modify these global variables.
     # This could be done differently... but for now we simply prevent it from being
@@ -717,7 +714,6 @@ def add_experimental_configvars():
 
 
 def add_error_and_warning_configvars():
-
     ###
     # To disable some warning about old bug that are fixed now.
     ###
@@ -1196,7 +1192,6 @@ def add_vm_configvars():
 
 
 def add_deprecated_configvars():
-
     # TODO: remove this? Agree
     config.add(
         "unittests__rseed",

pytensor/gradient.py

@@ -225,7 +225,6 @@ def Rop(
     # Check that each element of wrt corresponds to an element
     # of eval_points with the same dimensionality.
     for i, (wrt_elem, eval_point) in enumerate(zip(_wrt, _eval_points)):
-
         try:
             if wrt_elem.type.ndim != eval_point.type.ndim:
                 raise ValueError(
@@ -262,7 +261,6 @@ def _traverse(node):
                     # arguments, like for example random states
                     local_eval_points.append(None)
             elif inp.owner in seen_nodes:
-
                 local_eval_points.append(
                     seen_nodes[inp.owner][inp.owner.outputs.index(inp)]
                 )
@@ -937,7 +935,6 @@ def account_for(var):
             var_idx = app.outputs.index(var)
 
             for i, ipt in enumerate(app.inputs):
-
                 # don't process ipt if it is not a true
                 # parent of var
                 if not connection_pattern[i][var_idx]:
@@ -1048,7 +1045,6 @@ def access_term_cache(node):
         """Populates term_dict[node] and returns it"""
 
         if node not in term_dict:
-
             inputs = node.inputs
 
             output_grads = [access_grad_cache(var) for var in node.outputs]
@@ -1263,7 +1259,6 @@ def try_to_copy_if_needed(var):
             ]
 
             for i, term in enumerate(input_grads):
-
                 # Disallow Nones
                 if term is None:
                     # We don't know what None means. in the past it has been
@@ -1377,7 +1372,6 @@ def access_grad_cache(var):
                 node_to_idx = var_to_app_to_idx[var]
                 for node in node_to_idx:
                     for idx in node_to_idx[node]:
-
                         term = access_term_cache(node)[idx]
 
                         if not isinstance(term, Variable):
@@ -1858,7 +1852,6 @@ def random_projection():
         )
 
         if max_abs_err > abs_tol and max_rel_err > rel_tol:
-
             raise GradientError(
                 max_arg,
                 max_err_pos,
@@ -2042,7 +2035,6 @@ def hessian(cost, wrt, consider_constant=None, disconnected_inputs="raise"):
 
     hessians = []
     for input in wrt:
-
         if not isinstance(input, Variable):
             raise TypeError("hessian expects a (list of) Variable as `wrt`")
 

pytensor/graph/basic.py

@@ -823,7 +823,6 @@ def walk(
         node_hash: int = hash_fn(node)
 
         if node_hash not in rval_set:
-
             rval_set.add(node_hash)
 
             new_nodes: Optional[Iterable[T]] = expand(node)
@@ -1323,7 +1322,6 @@ def general_toposort(
 
     """
     if compute_deps_cache is None:
-
         if deps_cache is None:
             deps_cache = {}
 
@@ -1460,7 +1458,6 @@ def compute_deps_cache(obj):
             return rval
 
     else:
-
         # the inputs are used only here in the function that decides what
         # 'predecessors' to explore
         def compute_deps(obj):
@@ -1511,7 +1508,6 @@ def io_connection_pattern(inputs, outputs):
     # inputs and, for every node, infer their connection pattern to
     # every input from the connection patterns of their parents.
     for n in inner_nodes:
-
         # Get the connection pattern of the inner node's op. If the op
         # does not define a connection_pattern method, assume that
         # every node output is connected to every node input
@@ -1872,15 +1868,13 @@ def compare_nodes(nd_x, nd_y, common, different):
             # Compare the individual inputs for equality
             for dx, dy in zip(nd_x.inputs, nd_y.inputs):
                 if (dx, dy) not in common:
-
                     # Equality between the variables is unknown, compare
                     # their respective owners, if they have some
                     if (
                         dx.owner
                         and dy.owner
                         and dx.owner.outputs.index(dx) == dy.owner.outputs.index(dy)
                     ):
-
                         nodes_equal = compare_nodes(
                             dx.owner, dy.owner, common, different
                         )
@@ -1891,7 +1885,6 @@ def compare_nodes(nd_x, nd_y, common, different):
                     # If both variables don't have an owner, then they are
                     # inputs and can be directly compared
                     elif dx.owner is None and dy.owner is None:
-
                         if dx != dy:
                             if isinstance(dx, Constant) and isinstance(dy, Constant):
                                 if not dx.equals(dy):