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40,886 | pyglove.core.symbolic.base | infer | Returns the inferred value.
Args:
**kwargs: Optional keyword arguments for inference, which are usually
inferential subclass specific.
Returns:
Inferred value.
Raises:
AttributeError: If the value cannot be inferred.
| @abc.abstractmethod
def infer(self, **kwargs) -> Any:
"""Returns the inferred value.
Args:
**kwargs: Optional keyword arguments for inference, which are usually
inferential subclass specific.
Returns:
Inferred value.
Raises:
AttributeError: If the value cannot be inferred.
"""
| (self, **kwargs) -> Any | [
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|
40,887 | pyglove.core.symbolic.base | sym_setparent | Sets the parent of this object. | @abc.abstractmethod
def sym_setparent(self, parent: Optional['TopologyAware']) -> None:
"""Sets the parent of this object."""
| (self, parent: Optional[pyglove.core.symbolic.base.TopologyAware]) -> NoneType | [
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|
40,888 | pyglove.core.symbolic.base | sym_setpath | Sets the path of this object under its topology. | @abc.abstractmethod
def sym_setpath(self, path: object_utils.KeyPath) -> None:
"""Sets the path of this object under its topology."""
| (self, path: pyglove.core.object_utils.value_location.KeyPath) -> NoneType | [
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|
40,889 | pyglove.core.symbolic.inferred | InferredValue | Base class for inferred values. | class InferredValue(Object, base.Inferential):
"""Base class for inferred values."""
def custom_apply(self, *args, **kwargs: Any) -> Tuple[bool, Any]:
# This is to make a ``InferredValue`` object assignable
# to any symbolic attribute.
return (False, self)
| () | [
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|
40,925 | pyglove.core.symbolic.inferred | custom_apply | null | def custom_apply(self, *args, **kwargs: Any) -> Tuple[bool, Any]:
# This is to make a ``InferredValue`` object assignable
# to any symbolic attribute.
return (False, self)
| (self, *args, **kwargs: Any) -> Tuple[bool, Any] | [
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|
40,964 | pyglove.core.symbolic.list | Insertion | Class that marks a value to insert into a list.
Example::
l = pg.List([0, 1])
l.rebind({
0: pg.Insertion(2)
})
assert l == [2, 0, 1]
| class Insertion:
"""Class that marks a value to insert into a list.
Example::
l = pg.List([0, 1])
l.rebind({
0: pg.Insertion(2)
})
assert l == [2, 0, 1]
"""
value: Any
| (value: Any) -> None | [
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|
40,965 | pyglove.core.symbolic.list | __eq__ | null | # Copyright 2022 The PyGlove Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Symbolic list."""
import dataclasses
import math
import numbers
import typing
from typing import Any, Callable, Dict, Iterable, Iterator, Optional, Tuple, Union
from pyglove.core import object_utils
from pyglove.core import typing as pg_typing
from pyglove.core.symbolic import base
from pyglove.core.symbolic import flags
class List(list, base.Symbolic, pg_typing.CustomTyping):
"""Symbolic list.
``pg.List`` implements a list type whose instances are symbolically
programmable, which is a subclass of the built-in Python ``list``,
and the subclass of ``pg.Symbolic``.
``pg.List`` can be used as a regular list::
# Construct a symbolic list from an iterable object.
l = pg.List(range(10))
It also supports symbolic validation through the ``value_spec`` argument::
l = pg.List([1, 2, 3], value_spec=pg.typing.List(
pg.typing.Int(min_value=1),
max_size=10
))
# Raises: 0 is not in acceptable range.
l.append(0)
And can be symbolically manipulated::
l = pg.List([{'foo': 1}])
l.rebind({
'[0].foo': 2
})
pg.query(l, where=lambda x: isinstance(x, int))
The user call also subscribe changes to its sub-nodes::
def on_change(updates):
print(updates)
l = pg.List([{'foo': 1}], onchange_callaback=on_change)
# `on_change` will be triggered on item insertion.
l.append({'bar': 2})
# `on_change` will be triggered on item removal.
l.pop(0)
# `on_change` will also be triggered on subtree change.
l.rebind({'[0].bar': 3})
"""
@classmethod
def partial(cls,
items: Optional[Iterable[Any]] = None,
*,
value_spec: Optional[pg_typing.List] = None,
onchange_callback: Optional[Callable[
[Dict[object_utils.KeyPath, base.FieldUpdate]], None]] = None,
**kwargs) -> 'List':
"""Class method that creates a partial List object."""
return cls(items,
value_spec=value_spec,
onchange_callback=onchange_callback,
allow_partial=True,
**kwargs)
@classmethod
def from_json(cls,
json_value: Any,
*,
value_spec: Optional[pg_typing.List] = None,
allow_partial: bool = False,
root_path: Optional[object_utils.KeyPath] = None,
**kwargs) -> 'List':
"""Class method that load an symbolic List from a JSON value.
Example::
l = List.from_json([{
'_type': '__main__.Foo',
'f1': 1,
'f2': {
'f21': True
}
},
1
])
assert l.value_spec is None
# Okay:
l.append('abc')
# [0].f2 is bound by class Foo's field 'f2' definition
# (assuming it defines a schema for the Dict field).
assert l[0].f2.value_spec is not None
# Not okay:
l[0].f2.abc = 1
Args:
json_value: Input JSON value, only JSON list is acceptable.
value_spec: An optional `pg.typing.List` object as the schema for the
list.
allow_partial: Whether to allow elements of the list to be partial.
root_path: KeyPath of loaded object in its object tree.
**kwargs: Allow passing through keyword arguments that are not applicable.
Returns:
A schema-less symbolic list, but its items maybe symbolic.
"""
return cls(json_value,
value_spec=value_spec,
allow_partial=allow_partial,
root_path=root_path)
def __init__(
self,
items: Optional[Iterable[Any]] = None,
*,
value_spec: Optional[pg_typing.List] = None,
onchange_callback: Optional[Callable[
[Dict[object_utils.KeyPath, base.FieldUpdate]], None]] = None,
allow_partial: bool = False,
accessor_writable: bool = True,
sealed: bool = False,
root_path: Optional[object_utils.KeyPath] = None):
"""Constructor.
Args:
items: A optional iterable object as initial value for this list.
value_spec: Value spec that applies to this List.
onchange_callback: Callback when sub-tree has been modified.
allow_partial: Whether to allow unbound or partial fields. This takes
effect only when value_spec is not None.
accessor_writable: Whether to allow modification of this List using
accessors (operator[]).
sealed: Whether to seal this List after creation.
root_path: KeyPath of this List in its object tree.
"""
if value_spec and not isinstance(value_spec, pg_typing.List):
raise TypeError(
f'Argument \'value_spec\' must be a `pg.typing.List` object. '
f'Encountered {value_spec}.')
# We delay seal operation until items are filled.
base.Symbolic.__init__(
self,
allow_partial=allow_partial,
accessor_writable=accessor_writable,
sealed=False,
root_path=root_path)
self._value_spec = None
self._onchange_callback = None
list.__init__(self)
if items:
# Copy the symbolic form instead of evaluated form.
if isinstance(items, List):
items = items.sym_values()
for item in items:
self._set_item_without_permission_check(len(self), item)
if value_spec:
self.use_value_spec(value_spec, allow_partial)
# NOTE(daiyip): We set onchange callback at the end of init to avoid
# triggering during initialization.
self._onchange_callback = onchange_callback
self.seal(sealed)
@property
def max_size(self) -> Optional[int]:
"""Returns max size of this list."""
if self._value_spec:
return typing.cast(pg_typing.ListKey,
self._value_spec.element.key).max_value
return None
def use_value_spec(self,
value_spec: Optional[pg_typing.List],
allow_partial: bool = False) -> 'List':
"""Applies a ``pg.List`` as the value spec for current list.
Args:
value_spec: A List ValueSpec to apply to this List.
If current List is schema-less (whose immediate members are not
validated against schema), and `value_spec` is not None, the value spec
will be applied to the List.
Or else if current List is already symbolic (whose immediate members
are under the constraint of a List value spec), and `value_spec` is
None, current List will become schema-less. However, the schema
constraints for non-immediate members will remain.
allow_partial: Whether allow partial dict based on the schema. This flag
will override allow_partial flag in __init__ for spec-less List.
Returns:
Self.
Raises:
ValueError: schema validation failed due to value error.
RuntimeError: List is already bound with another value_spec.
TypeError: type errors during validation.
KeyError: key errors during validation.
"""
if value_spec is None:
self._value_spec = None
self._accessor_writable = True
return self
if not isinstance(value_spec, pg_typing.List):
raise ValueError(
self._error_message(
f'Value spec for list must be a `pg.typing.List` object. '
f'Encountered: {value_spec!r}'))
if self._value_spec and self._value_spec != value_spec:
raise RuntimeError(
self._error_message(
f'List is already bound with a different value '
f'spec: {self._value_spec}. New value spec: {value_spec}.'))
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|
40,967 | pyglove.core.symbolic.list | __repr__ | null | def use_value_spec(self,
value_spec: Optional[pg_typing.List],
allow_partial: bool = False) -> 'List':
"""Applies a ``pg.List`` as the value spec for current list.
Args:
value_spec: A List ValueSpec to apply to this List.
If current List is schema-less (whose immediate members are not
validated against schema), and `value_spec` is not None, the value spec
will be applied to the List.
Or else if current List is already symbolic (whose immediate members
are under the constraint of a List value spec), and `value_spec` is
None, current List will become schema-less. However, the schema
constraints for non-immediate members will remain.
allow_partial: Whether allow partial dict based on the schema. This flag
will override allow_partial flag in __init__ for spec-less List.
Returns:
Self.
Raises:
ValueError: schema validation failed due to value error.
RuntimeError: List is already bound with another value_spec.
TypeError: type errors during validation.
KeyError: key errors during validation.
"""
if value_spec is None:
self._value_spec = None
self._accessor_writable = True
return self
if not isinstance(value_spec, pg_typing.List):
raise ValueError(
self._error_message(
f'Value spec for list must be a `pg.typing.List` object. '
f'Encountered: {value_spec!r}'))
if self._value_spec and self._value_spec != value_spec:
raise RuntimeError(
self._error_message(
f'List is already bound with a different value '
f'spec: {self._value_spec}. New value spec: {value_spec}.'))
self._allow_partial = allow_partial
if flags.is_type_check_enabled():
# NOTE(daiyip): self._value_spec will be set in List.custom_apply method
# called by spec.apply, thus we don't need to set the _value_spec
# explicitly.
value_spec.apply(
self,
allow_partial=base.accepts_partial(self),
child_transform=base.symbolic_transform_fn(self._allow_partial),
root_path=self.sym_path)
else:
self._value_spec = value_spec
return self
| (self) | [
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|
40,968 | pyglove.core.object_utils.json_conversion | JSONConvertible | Interface for classes whose instances are convertible from/to JSON.
A JSON convertible object is an object that can be converted into plain Python
objects, hence can be serialized into or deserialized from JSON.
Subclasses of ``JSONConvertible`` should implement:
* ``to_json``: A method that returns a plain Python dict with a `_type`
property whose value should identify the class.
* ``from_json``: A class method that takes a plain Python dict and returns
an instance of the class.
Example::
class MyObject(pg.JSONConvertible):
def __init__(self, x: int):
self.x = x
def to_json(self, **kwargs):
return {
'_type': 'MyObject',
'x': self.x
}
@classmethod
def from_json(cls, json_value, **kwargs):
return cls(json_value['x'])
All symbolic types (see :class:`pyglove.Symbolic`) are JSON convertible.
| class JSONConvertible(metaclass=abc.ABCMeta):
"""Interface for classes whose instances are convertible from/to JSON.
A JSON convertible object is an object that can be converted into plain Python
objects, hence can be serialized into or deserialized from JSON.
Subclasses of ``JSONConvertible`` should implement:
* ``to_json``: A method that returns a plain Python dict with a `_type`
property whose value should identify the class.
* ``from_json``: A class method that takes a plain Python dict and returns
an instance of the class.
Example::
class MyObject(pg.JSONConvertible):
def __init__(self, x: int):
self.x = x
def to_json(self, **kwargs):
return {
'_type': 'MyObject',
'x': self.x
}
@classmethod
def from_json(cls, json_value, **kwargs):
return cls(json_value['x'])
All symbolic types (see :class:`pyglove.Symbolic`) are JSON convertible.
"""
# Registry for looking up the type definition for a string identifier during
# deserialization. One key can be used for only one type, while the same type
# can be registered with many different string identifiers, which can be
# useful to allow backward compatibility of existing serialized strings.
_TYPE_REGISTRY = _TypeRegistry()
# Key in serialized dict that represents the class to restore.
TYPE_NAME_KEY = '_type'
# Marker (as the first element of a list) for serializing tuples.
TUPLE_MARKER = '__tuple__'
# Type converter that converts a complex type to basic JSON value type.
# When this field is set by users, the converter will be invoked when a
# complex value cannot be serialized by existing methods.
TYPE_CONVERTER: Optional[
Callable[[Type[Any]], Callable[[Any], JSONValueType]]] = None
# Class property that indicates whether to automatically register class
# for deserialization. Subclass can override.
auto_register = True
@classmethod
def from_json(cls, json_value: JSONValueType, **kwargs) -> 'JSONConvertible':
"""Creates an instance of this class from a plain Python value.
NOTE(daiyip): ``pg.Symbolic`` overrides ``from_json`` class method.
Args:
json_value: JSON value type.
**kwargs: Keyword arguments as flags to control object creation.
Returns:
An instance of cls.
"""
del kwargs
assert isinstance(json_value, dict)
init_args = {k: from_json(v) for k, v in json_value.items()
if k != JSONConvertible.TYPE_NAME_KEY}
return cls(**init_args)
@abc.abstractmethod
def to_json(self, **kwargs) -> JSONValueType:
"""Returns a plain Python value as a representation for this object.
A plain Python value are basic python types that can be serialized into
JSON, e.g: ``bool``, ``int``, ``float``, ``str``, ``dict`` (with string
keys), ``list``, ``tuple`` where the container types should have plain
Python values as their values.
Args:
**kwargs: Keyword arguments as flags to control JSON conversion.
Returns:
A plain Python value.
"""
@classmethod
def register(
cls,
type_name: str,
subclass: Type['JSONConvertible'],
override_existing: bool = False
) -> None:
"""Registers a class with a type name.
The type name will be used as the key for class lookup during
deserialization. A class can be registered with multiple type names, but
a type name should be uesd only for one class.
Args:
type_name: A global unique string identifier for subclass.
subclass: A subclass of JSONConvertible.
override_existing: If True, override the class if the type name is
already present in the registry. Otherwise an error will be raised.
"""
cls._TYPE_REGISTRY.register(type_name, subclass, override_existing)
@classmethod
def add_module_alias(cls, source_name: str, target_name: str) -> None:
"""Adds a module alias so previous serialized objects could be loaded."""
cls._TYPE_REGISTRY.add_module_alias(source_name, target_name)
@classmethod
def is_registered(cls, type_name: str) -> bool:
"""Returns True if a type name is registered. Otherwise False."""
return cls._TYPE_REGISTRY.is_registered(type_name)
@classmethod
def class_from_typename(
cls, type_name: str) -> Optional[Type['JSONConvertible']]:
"""Gets the class for a registered type name.
Args:
type_name: A string as the global unique type identifier for requested
class.
Returns:
A type object if registered, otherwise None.
"""
return cls._TYPE_REGISTRY.class_from_typename(type_name)
@classmethod
def registered_types(cls) -> Iterable[Tuple[str, Type['JSONConvertible']]]:
"""Returns an iterator of registered (serialization key, class) tuples."""
return cls._TYPE_REGISTRY.iteritems()
@classmethod
def to_json_dict(
cls,
fields: Dict[str, Union[Tuple[Any, Any], Any]],
*,
exclude_default=False,
exclude_keys: Optional[Set[str]] = None,
**kwargs) -> Dict[str, JSONValueType]:
"""Helper method to create JSON dict from class and field."""
json_dict = {JSONConvertible.TYPE_NAME_KEY: _serialization_key(cls)}
exclude_keys = exclude_keys or set()
if exclude_default:
for k, (v, default) in fields.items():
if k not in exclude_keys and v != default:
json_dict[k] = to_json(v, **kwargs)
else:
json_dict.update(
{k: to_json(v, **kwargs) for k, v in fields.items()
if k not in exclude_keys})
return json_dict
def __init_subclass__(cls):
super().__init_subclass__()
if not inspect.isabstract(cls) and cls.auto_register:
type_name = _serialization_key(cls)
JSONConvertible.register(type_name, cls, override_existing=True)
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|
40,970 | pyglove.core.object_utils.json_conversion | to_json | Returns a plain Python value as a representation for this object.
A plain Python value are basic python types that can be serialized into
JSON, e.g: ``bool``, ``int``, ``float``, ``str``, ``dict`` (with string
keys), ``list``, ``tuple`` where the container types should have plain
Python values as their values.
Args:
**kwargs: Keyword arguments as flags to control JSON conversion.
Returns:
A plain Python value.
| @abc.abstractmethod
def to_json(self, **kwargs) -> JSONValueType:
"""Returns a plain Python value as a representation for this object.
A plain Python value are basic python types that can be serialized into
JSON, e.g: ``bool``, ``int``, ``float``, ``str``, ``dict`` (with string
keys), ``list``, ``tuple`` where the container types should have plain
Python values as their values.
Args:
**kwargs: Keyword arguments as flags to control JSON conversion.
Returns:
A plain Python value.
"""
| (self, **kwargs) -> Union[int, float, bool, str, List[Any], Dict[str, Any]] | [
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|
40,971 | pyglove.core.object_utils.value_location | KeyPath | Represents a path of keys from the root to a node in a tree.
``KeyPath`` is an important concept in PyGlove, which is used for representing
a symbolic object's location (see :meth:`pyglove.Symbolic.sym_path`) within
its symbolic tree. For example::
@pg.members([
('x', pg.typing.Int()),
('y', pg.typing.Str())
])
class A(pg.Object):
pass
@pg.members([
('z', pg.typing.Object(A))
])
class B(pg.Object):
pass
a = A(x=1, y='foo')
b = B(z=a)
assert a.sym_path == 'z' # The path to object `a` is 'z'.
assert b.sym_path == '' # The root object's KeyPath is empty.
Since each node in a tree has a unique location, given the root we shall be
able to use a ``KeyPath`` object to locate the node. With the example
above, we can query the member ``x`` of object ``a`` via::
pg.KeyPath.parse('z.x').query(b) # Should return 1.
Similarly, we can modify a symbolic object's sub-node based on a ``KeyPath``
object. See :meth:`pyglove.Symbolic.rebind` for modifying sub-nodes in a
symbolic tree.
| class KeyPath(common_traits.Formattable):
"""Represents a path of keys from the root to a node in a tree.
``KeyPath`` is an important concept in PyGlove, which is used for representing
a symbolic object's location (see :meth:`pyglove.Symbolic.sym_path`) within
its symbolic tree. For example::
@pg.members([
('x', pg.typing.Int()),
('y', pg.typing.Str())
])
class A(pg.Object):
pass
@pg.members([
('z', pg.typing.Object(A))
])
class B(pg.Object):
pass
a = A(x=1, y='foo')
b = B(z=a)
assert a.sym_path == 'z' # The path to object `a` is 'z'.
assert b.sym_path == '' # The root object's KeyPath is empty.
Since each node in a tree has a unique location, given the root we shall be
able to use a ``KeyPath`` object to locate the node. With the example
above, we can query the member ``x`` of object ``a`` via::
pg.KeyPath.parse('z.x').query(b) # Should return 1.
Similarly, we can modify a symbolic object's sub-node based on a ``KeyPath``
object. See :meth:`pyglove.Symbolic.rebind` for modifying sub-nodes in a
symbolic tree.
"""
def __init__(self,
key_or_key_list: Optional[Union[Any, List[Any]]] = None,
parent: Optional['KeyPath'] = None):
"""Constructor.
Args:
key_or_key_list: A single object as key, or a list/tuple of objects
as keys in the path.
When string types or StrKey objects are used as key, dot ('.') is used
as the delimiter, otherwise square brackets ('[]') is used as the
delimiter when formatting a KeyPath.
For object type key, str(object) will be used to represent the key in
string form.
parent: Parent KeyPath.
"""
if key_or_key_list is None:
key_or_key_list = []
elif not isinstance(key_or_key_list, (tuple, list)):
key_or_key_list = [key_or_key_list]
keys = []
if parent:
keys.extend(parent.keys)
keys.extend(key_or_key_list)
self._keys = keys
# NOTE(daiyip): Lazy to build path string cache for fast access.
self._path_str = None
@classmethod
def from_value(cls, value: Union['KeyPath', str, int]) -> 'KeyPath':
"""Returns a KeyPath object from a KeyPath equivalence."""
if isinstance(value, str):
value = cls.parse(value)
elif isinstance(value, int):
value = cls(value)
elif not isinstance(value, KeyPath):
raise ValueError(f'{value!r} is not a valid KeyPath equivalence.')
return value
@classmethod
def parse(cls,
path_str: str,
parent: Optional['KeyPath'] = None) -> 'KeyPath':
"""Creates a ``KeyPath`` object from parsing a JSONPath-like string.
The JSONPath (https://restfulapi.net/json-jsonpath/) like string is defined
as following::
<path> := <empty> | {<dict-key>[.<dict-key>]*}
<dict-key> := <identifier>['[('<list-key>|<special-dict-key>)']']*
<list-key> := <number>
<special-dict-key> := <string-with-delimiter-chars>
<delimiter_chars> := '[' | ']' | '.'
For example, following keys are valid path strings::
'' : An empty path representing the root of a path.
'a' : A path that contains a dict key 'a'.
'a.b' : A path that contains two dict keys 'a' and 'b'.
'a[0]' : A path that contains a dict key 'a' and a list key 0.
'a.0.' : A path that contains two dict keys 'a' and '0'.
'a[0][1]' : A path that contains a dict key 'a' and two list keys
0 and 1 for a multi-dimension list.
'a[x.y].b' : A path that contains three dict keys: 'a', 'x.y', 'b'.
Since 'x.y' has delimiter characters, it needs to be
enclosed in brackets.
TODO(daiyip): Support paring ``KeyPath`` from keys of complex types.
Now this method only supports parsing KeyPath of string and int keys.
That being said, ``format``/``parse`` are not symmetric, while ``format``
can convert a ``KeyPath`` that includes complex keys into a string,
``parse`` is not able to convert them back.
Args:
path_str: A JSON-path-like string.
parent: Parent KeyPath object.
Returns:
A KeyPath object.
Raises:
ValueError: Path string is in bad format.
"""
if not isinstance(path_str, str):
raise ValueError(
f'\'path_str\' must be a string type. Encountered: {path_str!r}')
keys = []
def _append_key(key, preserve_empty=False, maybe_numeric=False):
"""Helper method to append key."""
if not (preserve_empty or key):
return
if maybe_numeric and key.lstrip('-').isdigit():
key = int(key)
keys.append(key)
pos, key_start, unmatched_brackets = 0, 0, 0
while pos != len(path_str):
ch = path_str[pos]
if ch == ']':
unmatched_brackets -= 1
if unmatched_brackets == 0:
key = path_str[key_start:pos]
_append_key(key, True, True)
key_start = pos + 1
elif unmatched_brackets < 0:
raise ValueError(
f'KeyPath parse failed: unmatched close bracket at position '
f'{pos}:{path_str!r}')
elif ch == '[':
if unmatched_brackets == 0:
key = path_str[key_start:pos]
_append_key(key)
key_start = pos + 1
unmatched_brackets += 1
elif ch == '.' and unmatched_brackets == 0:
key = path_str[key_start:pos]
_append_key(key)
key_start = pos + 1
pos += 1
if key_start != len(path_str):
_append_key(path_str[key_start:])
if unmatched_brackets != 0:
raise ValueError(
f'KeyPath parse failed: unmatched open bracket at position '
f'{key_start - 1}: {path_str!r}')
return KeyPath(keys, parent)
@property
def keys(self) -> List[Any]:
"""A list of keys in this path."""
return copy.copy(self._keys)
@property
def key(self) -> Any:
"""The rightmost key of this path."""
if self.depth == 0:
raise KeyError('Key of root KeyPath does not exist.')
return self._keys[-1]
@property
def is_root(self) -> bool:
"""Returns True if this path is the root of a tree."""
return not self._keys
@property
def depth(self) -> int:
"""The depth of this path."""
return len(self._keys)
@property
def parent(self) -> 'KeyPath':
"""The ``KeyPath`` object for current node's parent.
Example::
path = pg.KeyPath.parse('a.b.c.')
assert path.parent == 'a.b'
Returns:
A ``KeyPath`` object for the parent of current node.
Raises:
KeyError: If current path is the root.
"""
if self.is_root:
raise KeyError('Parent of a root KeyPath does not exist.')
return KeyPath(self._keys[:-1])
def __sub__(self, other: Union[None, int, str, 'KeyPath']) -> 'KeyPath':
"""Finds the relative path of this path to the other.
Example::
path1 = pg.KeyPath.parse('a.b.c.d')
path2 = pg.KeyPath.parse('a.b')
assert path1 - path2 == 'c.d'
Args:
other: Object to subtract, which can be None, int (as a depth-1 KeyPath),
string (parsed as a KeyPath) or a KeyPath object.
Returns:
Relative path of this path to the other.
Raises:
ValueError: This path is an ancestor node of the other path,
or these two paths are in different branch.
"""
if other is None:
return self
if isinstance(other, str):
other = KeyPath.parse(other)
elif isinstance(other, int):
other = KeyPath(other)
if not isinstance(other, KeyPath):
raise TypeError(
f'Cannot subtract KeyPath({self}) by {other!r}.')
max_len = max(len(self), len(other))
for pos in range(max_len):
if pos >= len(self):
raise ValueError(
f'KeyPath subtraction failed: left path {self!r} '
f'is an ancestor of right path {other!r}.')
if pos >= len(other):
return KeyPath(self.keys[pos:])
if self.keys[pos] != other.keys[pos]:
raise ValueError(
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|
40,972 | pyglove.core.object_utils.value_location | __add__ | Concatenates a KeyPath equivalent object.
Args:
other: Object to add, which can be None, int (as a 1-level KeyPath),
string (parsed as a KeyPath), a KeyPath object, or any other object as
a single key.
Returns:
Newly concatenated KeyPath.
Raises:
ValueError: If other is a string that cannot be parsed into a KeyPath.
| def __add__(self, other: Any) -> 'KeyPath':
"""Concatenates a KeyPath equivalent object.
Args:
other: Object to add, which can be None, int (as a 1-level KeyPath),
string (parsed as a KeyPath), a KeyPath object, or any other object as
a single key.
Returns:
Newly concatenated KeyPath.
Raises:
ValueError: If other is a string that cannot be parsed into a KeyPath.
"""
if other is None:
return self
if isinstance(other, str):
other = KeyPath.parse(other)
elif not isinstance(other, KeyPath):
other = KeyPath(other)
assert isinstance(other, KeyPath)
return KeyPath(other.keys, self)
| (self, other: Any) -> pyglove.core.object_utils.value_location.KeyPath | [
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|
40,973 | pyglove.core.object_utils.value_location | __eq__ | Equality check.
Args:
other: A string or a KeyPath.
Returns:
Whether JSON-path representation (either absolute or relative form)
of current path equals to other.
| def __eq__(self, other: Any) -> bool:
"""Equality check.
Args:
other: A string or a KeyPath.
Returns:
Whether JSON-path representation (either absolute or relative form)
of current path equals to other.
"""
if isinstance(other, str):
return self.path == other
return isinstance(other, KeyPath) and self.keys == other.keys
| (self, other: Any) -> bool | [
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|
40,974 | pyglove.core.object_utils.value_location | __ge__ | null | def __ge__(self, other: Any) -> bool:
return self._compare(other, operator.ge)
| (self, other: Any) -> bool | [
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|
40,975 | pyglove.core.object_utils.value_location | __gt__ | null | def __gt__(self, other: Any) -> bool:
return self._compare(other, operator.gt)
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|
40,976 | pyglove.core.object_utils.value_location | __hash__ | Hash function.
Returns:
return the hash value of its path.
NOTE(daiyip): KeyPath shares the same hash of its JSONPath representation
(relative form), thus we can lookup a dict with KeyPath key by string,
and vice versa.
| def __hash__(self):
"""Hash function.
Returns:
return the hash value of its path.
NOTE(daiyip): KeyPath shares the same hash of its JSONPath representation
(relative form), thus we can lookup a dict with KeyPath key by string,
and vice versa.
"""
return hash(self.path)
| (self) | [
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|
40,977 | pyglove.core.object_utils.value_location | __init__ | Constructor.
Args:
key_or_key_list: A single object as key, or a list/tuple of objects
as keys in the path.
When string types or StrKey objects are used as key, dot ('.') is used
as the delimiter, otherwise square brackets ('[]') is used as the
delimiter when formatting a KeyPath.
For object type key, str(object) will be used to represent the key in
string form.
parent: Parent KeyPath.
| def __init__(self,
key_or_key_list: Optional[Union[Any, List[Any]]] = None,
parent: Optional['KeyPath'] = None):
"""Constructor.
Args:
key_or_key_list: A single object as key, or a list/tuple of objects
as keys in the path.
When string types or StrKey objects are used as key, dot ('.') is used
as the delimiter, otherwise square brackets ('[]') is used as the
delimiter when formatting a KeyPath.
For object type key, str(object) will be used to represent the key in
string form.
parent: Parent KeyPath.
"""
if key_or_key_list is None:
key_or_key_list = []
elif not isinstance(key_or_key_list, (tuple, list)):
key_or_key_list = [key_or_key_list]
keys = []
if parent:
keys.extend(parent.keys)
keys.extend(key_or_key_list)
self._keys = keys
# NOTE(daiyip): Lazy to build path string cache for fast access.
self._path_str = None
| (self, key_or_key_list: Union[Any, List[Any], NoneType] = None, parent: Optional[pyglove.core.object_utils.value_location.KeyPath] = None) | [
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|
40,978 | pyglove.core.object_utils.value_location | __le__ | null | def __le__(self, other: Any) -> bool:
return self._compare(other, operator.le)
| (self, other: Any) -> bool | [
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|
40,979 | pyglove.core.object_utils.value_location | __len__ | Use depth as length of current path. | def __len__(self) -> int:
"""Use depth as length of current path."""
return self.depth
| (self) -> int | [
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|
40,980 | pyglove.core.object_utils.value_location | __lt__ | null | def __lt__(self, other: Any) -> bool:
return self._compare(other, operator.lt)
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|
40,981 | pyglove.core.object_utils.value_location | __ne__ | null | def __ne__(self, other: Any) -> bool:
return not self.__eq__(other)
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|
40,984 | pyglove.core.object_utils.value_location | __sub__ | Finds the relative path of this path to the other.
Example::
path1 = pg.KeyPath.parse('a.b.c.d')
path2 = pg.KeyPath.parse('a.b')
assert path1 - path2 == 'c.d'
Args:
other: Object to subtract, which can be None, int (as a depth-1 KeyPath),
string (parsed as a KeyPath) or a KeyPath object.
Returns:
Relative path of this path to the other.
Raises:
ValueError: This path is an ancestor node of the other path,
or these two paths are in different branch.
| def __sub__(self, other: Union[None, int, str, 'KeyPath']) -> 'KeyPath':
"""Finds the relative path of this path to the other.
Example::
path1 = pg.KeyPath.parse('a.b.c.d')
path2 = pg.KeyPath.parse('a.b')
assert path1 - path2 == 'c.d'
Args:
other: Object to subtract, which can be None, int (as a depth-1 KeyPath),
string (parsed as a KeyPath) or a KeyPath object.
Returns:
Relative path of this path to the other.
Raises:
ValueError: This path is an ancestor node of the other path,
or these two paths are in different branch.
"""
if other is None:
return self
if isinstance(other, str):
other = KeyPath.parse(other)
elif isinstance(other, int):
other = KeyPath(other)
if not isinstance(other, KeyPath):
raise TypeError(
f'Cannot subtract KeyPath({self}) by {other!r}.')
max_len = max(len(self), len(other))
for pos in range(max_len):
if pos >= len(self):
raise ValueError(
f'KeyPath subtraction failed: left path {self!r} '
f'is an ancestor of right path {other!r}.')
if pos >= len(other):
return KeyPath(self.keys[pos:])
if self.keys[pos] != other.keys[pos]:
raise ValueError(
f'KeyPath subtraction failed: left path {self!r} '
f'and right path {other!r} are in different subtree.')
return KeyPath()
| (self, other: Union[NoneType, int, str, pyglove.core.object_utils.value_location.KeyPath]) -> pyglove.core.object_utils.value_location.KeyPath | [
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|
40,985 | pyglove.core.object_utils.value_location | _compare | Compare to another KeyPath or a string.
Args:
other: A Keypath or a string.
comparison: A comparison operator.
Returns:
Whether or not the comparison holds true.
Raises:
TypeError: The other object is neither a Keypath nor a string.
| def _compare(
self,
other: Any,
comparison: Callable[[Any, Any], bool]
) -> bool:
"""Compare to another KeyPath or a string.
Args:
other: A Keypath or a string.
comparison: A comparison operator.
Returns:
Whether or not the comparison holds true.
Raises:
TypeError: The other object is neither a Keypath nor a string.
"""
if isinstance(other, str):
return comparison(self.path, other)
if isinstance(other, KeyPath):
return comparison(
tuple(map(KeyPath._KeyComparisonWrapper, self.keys)),
tuple(map(KeyPath._KeyComparisonWrapper, other.keys))
)
raise TypeError(
f'Comparison is not supported between instances of \'KeyPath\' and '
f'{type(other).__name__!r}.')
| (self, other: Any, comparison: Callable[[Any, Any], bool]) -> bool | [
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|
40,986 | pyglove.core.object_utils.value_location | _has_special_chars | Returns True if key has special characters. | def _has_special_chars(self, key):
"""Returns True if key has special characters."""
return any([c in key for c in ['[', ']', '.']])
| (self, key) | [
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|
40,988 | pyglove.core.object_utils.value_location | _query | Query the value of current path up to key_pos from an object.
Args:
key_pos: Start position in self._keys.
src: Source value to query.
Returns:
Value from src if path exists.
Raises:
KeyError: Path doesn't exist in src.
| def _query(self, key_pos: int, src: Any) -> Any:
"""Query the value of current path up to key_pos from an object.
Args:
key_pos: Start position in self._keys.
src: Source value to query.
Returns:
Value from src if path exists.
Raises:
KeyError: Path doesn't exist in src.
"""
if key_pos == len(self._keys):
return src
key = self.keys[key_pos]
# NOTE(daiyip): For contextual value (e.g. ``pg.ContextualValue``),
# `query` returns its symbolic form instead of its evaluated value.
if hasattr(src, 'sym_getattr'):
assert hasattr(src, 'sym_hasattr')
if src.sym_hasattr(key):
return self._query(key_pos + 1, src.sym_getattr(key))
elif hasattr(src, '__getitem__'):
if isinstance(key, int):
if not hasattr(src, '__len__'):
raise KeyError(
f'Cannot query index ({key}) on object ({src!r}): '
f'\'__len__\' does not exist.')
if key < len(src):
return self._query(key_pos + 1, src[key])
else:
if not hasattr(src, '__contains__'):
raise KeyError(
f'Cannot query key ({key!r}) on object ({src!r}): '
f'\'__contains__\' does not exist.')
if key in src:
return self._query(key_pos + 1, src[key])
else:
raise KeyError(
f'Cannot query sub-key {key!r} of object ({src!r}): '
f'\'__getitem__\' does not exist. '
f'(path={KeyPath(self.keys[:key_pos])})')
raise KeyError(
f'Path {KeyPath(self._keys[:key_pos + 1])!r} does not exist: '
f'key {key!r} is absent from innermost value {src!r}.')
| (self, key_pos: int, src: Any) -> Any | [
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|
40,989 | pyglove.core.object_utils.value_location | exists | Returns whether current path exists in source object. | def exists(self, src: Any) -> bool:
"""Returns whether current path exists in source object."""
try:
self.query(src)
return True
except KeyError:
return False
| (self, src: Any) -> bool | [
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|
40,990 | pyglove.core.object_utils.value_location | format | Format current path. | def format(self, *args, **kwargs):
"""Format current path."""
return self.path
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|
40,991 | pyglove.core.object_utils.value_location | get | Gets the value of current path from an object with a default value. | def get(self, src: Any, default_value: Optional[Any] = None) -> Any:
"""Gets the value of current path from an object with a default value."""
try:
return self.query(src)
except KeyError:
return default_value
| (self, src: Any, default_value: Optional[Any] = None) -> Any | [
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|
40,992 | pyglove.core.object_utils.value_location | path_str | Returns JSONPath representation of current path.
Args:
preserve_complex_keys: if True, complex keys such as 'x.y' will be
preserved by quoted in brackets.
For example: KeyPath(['a', 'x.y', 'b']) will return 'a[x.y].b' when
`preserve_complex_keys` is True, and `a.x.y.b` when
`preserve_complex_keys` is False.
Returns:
Path string.
| def path_str(self, preserve_complex_keys: bool = True) -> str:
"""Returns JSONPath representation of current path.
Args:
preserve_complex_keys: if True, complex keys such as 'x.y' will be
preserved by quoted in brackets.
For example: KeyPath(['a', 'x.y', 'b']) will return 'a[x.y].b' when
`preserve_complex_keys` is True, and `a.x.y.b` when
`preserve_complex_keys` is False.
Returns:
Path string.
"""
s = []
for i, key in enumerate(self._keys):
if ((isinstance(key, str)
and not (preserve_complex_keys and self._has_special_chars(key)))
or isinstance(key, StrKey)):
if i != 0:
s.append('.')
s.append(str(key))
else:
s.append(f'[{key}]')
return ''.join(s)
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|
40,993 | pyglove.core.object_utils.value_location | query | Query the value from the source object based on current path.
Example::
@pg.members([
('x', pg.typing.Int()),
('y', pg.typing.Str())
])
class A(pg.Object):
pass
@pg.members([
('z', pg.typing.Object(A))
])
class B(pg.Object):
pass
b = B(z=A(x=1, y='foo'))
assert pg.KeyPath.parse('z.x').query(b) == 1
Args:
src: Source value to query.
Returns:
Value from src if path exists.
Raises:
KeyError: Path doesn't exist in src.
RuntimeError: Called on a KeyPath that is considered as removed.
| def query(self, src: Any) -> Any:
"""Query the value from the source object based on current path.
Example::
@pg.members([
('x', pg.typing.Int()),
('y', pg.typing.Str())
])
class A(pg.Object):
pass
@pg.members([
('z', pg.typing.Object(A))
])
class B(pg.Object):
pass
b = B(z=A(x=1, y='foo'))
assert pg.KeyPath.parse('z.x').query(b) == 1
Args:
src: Source value to query.
Returns:
Value from src if path exists.
Raises:
KeyError: Path doesn't exist in src.
RuntimeError: Called on a KeyPath that is considered as removed.
"""
return self._query(0, src)
| (self, src: Any) -> Any | [
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|
40,994 | pyglove.core.typing.class_schema | KeySpec | Interface for key specifications.
A key specification determines what keys are acceptable for a symbolic
field (see :class:`pyglove.Field`). Usually, symbolic attributes have an 1:1
relationship with symbolic fields. But in some cases (e.g. a dict with dynamic
keys), a field can be used to describe a group of symbolic attributes::
# A dictionary that accepts key 'x' with float value
# or keys started with 'foo' with int values.
d = pg.Dict(value_spec=pg.Dict([
('x', pg.typing.Float(min_value=0.0)),
(pg.typing.StrKey('foo.*'), pg.typing.Int())
]))
You may noticed that the code above pass a string 'x' for the key spec for a
field definition. The string is automatically converted to
:class:`pyglove.typing.ConstStrKey`.
PyGlove's Builtin key specifications are:
+---------------------------+----------------------------------------------+
| ``KeySpec`` type | Class |
+===========================+==============================================+
| Fixed string identifier | :class:`pyglove.typing.ConstStrKey` |
+---------------------------+----------------------------------------------+
| Dynamic string identifier | :class:`pyglove.typing.StrKey` |
+---------------------------+----------------------------------------------+
| Key of a list | :class:`pyglove.typing.ListKey` |
+---------------------------+----------------------------------------------+
| Key of a tuple | :class:`pyglove.typing.TupleKey` |
+---------------------------+----------------------------------------------+
In most scenarios, the user either use a string or a ``StrKey`` as the key
spec, while other ``KeySpec`` subclasses (e.g. ``ListKey`` and ``TupleKey``)
are used internally to constrain list size and tuple items.
| class KeySpec(object_utils.Formattable, object_utils.JSONConvertible):
"""Interface for key specifications.
A key specification determines what keys are acceptable for a symbolic
field (see :class:`pyglove.Field`). Usually, symbolic attributes have an 1:1
relationship with symbolic fields. But in some cases (e.g. a dict with dynamic
keys), a field can be used to describe a group of symbolic attributes::
# A dictionary that accepts key 'x' with float value
# or keys started with 'foo' with int values.
d = pg.Dict(value_spec=pg.Dict([
('x', pg.typing.Float(min_value=0.0)),
(pg.typing.StrKey('foo.*'), pg.typing.Int())
]))
You may noticed that the code above pass a string 'x' for the key spec for a
field definition. The string is automatically converted to
:class:`pyglove.typing.ConstStrKey`.
PyGlove's Builtin key specifications are:
+---------------------------+----------------------------------------------+
| ``KeySpec`` type | Class |
+===========================+==============================================+
| Fixed string identifier | :class:`pyglove.typing.ConstStrKey` |
+---------------------------+----------------------------------------------+
| Dynamic string identifier | :class:`pyglove.typing.StrKey` |
+---------------------------+----------------------------------------------+
| Key of a list | :class:`pyglove.typing.ListKey` |
+---------------------------+----------------------------------------------+
| Key of a tuple | :class:`pyglove.typing.TupleKey` |
+---------------------------+----------------------------------------------+
In most scenarios, the user either use a string or a ``StrKey`` as the key
spec, while other ``KeySpec`` subclasses (e.g. ``ListKey`` and ``TupleKey``)
are used internally to constrain list size and tuple items.
"""
@property
@abc.abstractmethod
def is_const(self) -> bool:
"""Returns whether current key is const."""
@abc.abstractmethod
def match(self, key: Any) -> bool:
"""Returns whether current key specification can match a key."""
@abc.abstractmethod
def extend(self, base: 'KeySpec') -> 'KeySpec':
"""Extend base key specification and returns self.
NOTE(daiyip): When a ``Field`` extends a base Field (from a base schema),
it calls ``extend`` on both its ``KeySpec`` and ``ValueSpec``.
``KeySpec.extend`` is to determine whether the ``Field`` key is allowed to
be extended, and ``ValueSpec.extend`` is to determine the final
``ValueSpec`` after extension.
Args:
base: A base ``KeySpec`` object.
Returns:
An ``KeySpec`` object derived from this key spec by extending the base.
"""
@classmethod
def from_str(cls, key: str) -> 'KeySpec':
"""Get a concrete ValueSpec from annotation."""
del key
assert False, 'Overridden in `key_specs.py`.'
| () | [
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|
40,999 | pyglove.core.typing.class_schema | extend | Extend base key specification and returns self.
NOTE(daiyip): When a ``Field`` extends a base Field (from a base schema),
it calls ``extend`` on both its ``KeySpec`` and ``ValueSpec``.
``KeySpec.extend`` is to determine whether the ``Field`` key is allowed to
be extended, and ``ValueSpec.extend`` is to determine the final
``ValueSpec`` after extension.
Args:
base: A base ``KeySpec`` object.
Returns:
An ``KeySpec`` object derived from this key spec by extending the base.
| @abc.abstractmethod
def extend(self, base: 'KeySpec') -> 'KeySpec':
"""Extend base key specification and returns self.
NOTE(daiyip): When a ``Field`` extends a base Field (from a base schema),
it calls ``extend`` on both its ``KeySpec`` and ``ValueSpec``.
``KeySpec.extend`` is to determine whether the ``Field`` key is allowed to
be extended, and ``ValueSpec.extend`` is to determine the final
``ValueSpec`` after extension.
Args:
base: A base ``KeySpec`` object.
Returns:
An ``KeySpec`` object derived from this key spec by extending the base.
"""
| (self, base: pyglove.core.typing.class_schema.KeySpec) -> pyglove.core.typing.class_schema.KeySpec | [
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|
41,001 | pyglove.core.typing.class_schema | match | Returns whether current key specification can match a key. | @abc.abstractmethod
def match(self, key: Any) -> bool:
"""Returns whether current key specification can match a key."""
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|
41,003 | pyglove.core.symbolic.list | List | Symbolic list.
``pg.List`` implements a list type whose instances are symbolically
programmable, which is a subclass of the built-in Python ``list``,
and the subclass of ``pg.Symbolic``.
``pg.List`` can be used as a regular list::
# Construct a symbolic list from an iterable object.
l = pg.List(range(10))
It also supports symbolic validation through the ``value_spec`` argument::
l = pg.List([1, 2, 3], value_spec=pg.typing.List(
pg.typing.Int(min_value=1),
max_size=10
))
# Raises: 0 is not in acceptable range.
l.append(0)
And can be symbolically manipulated::
l = pg.List([{'foo': 1}])
l.rebind({
'[0].foo': 2
})
pg.query(l, where=lambda x: isinstance(x, int))
The user call also subscribe changes to its sub-nodes::
def on_change(updates):
print(updates)
l = pg.List([{'foo': 1}], onchange_callaback=on_change)
# `on_change` will be triggered on item insertion.
l.append({'bar': 2})
# `on_change` will be triggered on item removal.
l.pop(0)
# `on_change` will also be triggered on subtree change.
l.rebind({'[0].bar': 3})
| class List(list, base.Symbolic, pg_typing.CustomTyping):
"""Symbolic list.
``pg.List`` implements a list type whose instances are symbolically
programmable, which is a subclass of the built-in Python ``list``,
and the subclass of ``pg.Symbolic``.
``pg.List`` can be used as a regular list::
# Construct a symbolic list from an iterable object.
l = pg.List(range(10))
It also supports symbolic validation through the ``value_spec`` argument::
l = pg.List([1, 2, 3], value_spec=pg.typing.List(
pg.typing.Int(min_value=1),
max_size=10
))
# Raises: 0 is not in acceptable range.
l.append(0)
And can be symbolically manipulated::
l = pg.List([{'foo': 1}])
l.rebind({
'[0].foo': 2
})
pg.query(l, where=lambda x: isinstance(x, int))
The user call also subscribe changes to its sub-nodes::
def on_change(updates):
print(updates)
l = pg.List([{'foo': 1}], onchange_callaback=on_change)
# `on_change` will be triggered on item insertion.
l.append({'bar': 2})
# `on_change` will be triggered on item removal.
l.pop(0)
# `on_change` will also be triggered on subtree change.
l.rebind({'[0].bar': 3})
"""
@classmethod
def partial(cls,
items: Optional[Iterable[Any]] = None,
*,
value_spec: Optional[pg_typing.List] = None,
onchange_callback: Optional[Callable[
[Dict[object_utils.KeyPath, base.FieldUpdate]], None]] = None,
**kwargs) -> 'List':
"""Class method that creates a partial List object."""
return cls(items,
value_spec=value_spec,
onchange_callback=onchange_callback,
allow_partial=True,
**kwargs)
@classmethod
def from_json(cls,
json_value: Any,
*,
value_spec: Optional[pg_typing.List] = None,
allow_partial: bool = False,
root_path: Optional[object_utils.KeyPath] = None,
**kwargs) -> 'List':
"""Class method that load an symbolic List from a JSON value.
Example::
l = List.from_json([{
'_type': '__main__.Foo',
'f1': 1,
'f2': {
'f21': True
}
},
1
])
assert l.value_spec is None
# Okay:
l.append('abc')
# [0].f2 is bound by class Foo's field 'f2' definition
# (assuming it defines a schema for the Dict field).
assert l[0].f2.value_spec is not None
# Not okay:
l[0].f2.abc = 1
Args:
json_value: Input JSON value, only JSON list is acceptable.
value_spec: An optional `pg.typing.List` object as the schema for the
list.
allow_partial: Whether to allow elements of the list to be partial.
root_path: KeyPath of loaded object in its object tree.
**kwargs: Allow passing through keyword arguments that are not applicable.
Returns:
A schema-less symbolic list, but its items maybe symbolic.
"""
return cls(json_value,
value_spec=value_spec,
allow_partial=allow_partial,
root_path=root_path)
def __init__(
self,
items: Optional[Iterable[Any]] = None,
*,
value_spec: Optional[pg_typing.List] = None,
onchange_callback: Optional[Callable[
[Dict[object_utils.KeyPath, base.FieldUpdate]], None]] = None,
allow_partial: bool = False,
accessor_writable: bool = True,
sealed: bool = False,
root_path: Optional[object_utils.KeyPath] = None):
"""Constructor.
Args:
items: A optional iterable object as initial value for this list.
value_spec: Value spec that applies to this List.
onchange_callback: Callback when sub-tree has been modified.
allow_partial: Whether to allow unbound or partial fields. This takes
effect only when value_spec is not None.
accessor_writable: Whether to allow modification of this List using
accessors (operator[]).
sealed: Whether to seal this List after creation.
root_path: KeyPath of this List in its object tree.
"""
if value_spec and not isinstance(value_spec, pg_typing.List):
raise TypeError(
f'Argument \'value_spec\' must be a `pg.typing.List` object. '
f'Encountered {value_spec}.')
# We delay seal operation until items are filled.
base.Symbolic.__init__(
self,
allow_partial=allow_partial,
accessor_writable=accessor_writable,
sealed=False,
root_path=root_path)
self._value_spec = None
self._onchange_callback = None
list.__init__(self)
if items:
# Copy the symbolic form instead of evaluated form.
if isinstance(items, List):
items = items.sym_values()
for item in items:
self._set_item_without_permission_check(len(self), item)
if value_spec:
self.use_value_spec(value_spec, allow_partial)
# NOTE(daiyip): We set onchange callback at the end of init to avoid
# triggering during initialization.
self._onchange_callback = onchange_callback
self.seal(sealed)
@property
def max_size(self) -> Optional[int]:
"""Returns max size of this list."""
if self._value_spec:
return typing.cast(pg_typing.ListKey,
self._value_spec.element.key).max_value
return None
def use_value_spec(self,
value_spec: Optional[pg_typing.List],
allow_partial: bool = False) -> 'List':
"""Applies a ``pg.List`` as the value spec for current list.
Args:
value_spec: A List ValueSpec to apply to this List.
If current List is schema-less (whose immediate members are not
validated against schema), and `value_spec` is not None, the value spec
will be applied to the List.
Or else if current List is already symbolic (whose immediate members
are under the constraint of a List value spec), and `value_spec` is
None, current List will become schema-less. However, the schema
constraints for non-immediate members will remain.
allow_partial: Whether allow partial dict based on the schema. This flag
will override allow_partial flag in __init__ for spec-less List.
Returns:
Self.
Raises:
ValueError: schema validation failed due to value error.
RuntimeError: List is already bound with another value_spec.
TypeError: type errors during validation.
KeyError: key errors during validation.
"""
if value_spec is None:
self._value_spec = None
self._accessor_writable = True
return self
if not isinstance(value_spec, pg_typing.List):
raise ValueError(
self._error_message(
f'Value spec for list must be a `pg.typing.List` object. '
f'Encountered: {value_spec!r}'))
if self._value_spec and self._value_spec != value_spec:
raise RuntimeError(
self._error_message(
f'List is already bound with a different value '
f'spec: {self._value_spec}. New value spec: {value_spec}.'))
self._allow_partial = allow_partial
if flags.is_type_check_enabled():
# NOTE(daiyip): self._value_spec will be set in List.custom_apply method
# called by spec.apply, thus we don't need to set the _value_spec
# explicitly.
value_spec.apply(
self,
allow_partial=base.accepts_partial(self),
child_transform=base.symbolic_transform_fn(self._allow_partial),
root_path=self.sym_path)
else:
self._value_spec = value_spec
return self
@property
def value_spec(self) -> Optional[pg_typing.List]:
"""Returns value spec of this List."""
return self._value_spec
def sym_attr_field(self, key: Union[str, int]) -> Optional[pg_typing.Field]:
"""Returns the field definition for a symbolic attribute."""
del key
if self._value_spec is None:
return None
return self._value_spec.element
def sym_hasattr(sel | (items: Optional[Iterable[Any]] = None, *, value_spec: Optional[pyglove.core.typing.value_specs.List] = None, onchange_callback: Optional[Callable[[Dict[pyglove.core.object_utils.value_location.KeyPath, pyglove.core.symbolic.base.FieldUpdate]], NoneType]] = None, allow_partial: bool = False, accessor_writable: bool = True, sealed: bool = False, root_path: Optional[pyglove.core.object_utils.value_location.KeyPath] = None) | [
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|
41,005 | pyglove.core.symbolic.list | __add__ | Returns a concatenated List of self and other. | def __add__(self, other: Iterable[Any]) -> 'List':
"""Returns a concatenated List of self and other."""
concatenated = self.copy()
concatenated.extend(other)
return concatenated
| (self, other: Iterable[Any]) -> pyglove.core.symbolic.list.List | [
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|
41,006 | pyglove.core.symbolic.list | __copy__ | List.copy. | def __copy__(self) -> 'List':
"""List.copy."""
return self.sym_clone(deep=False)
| (self) -> pyglove.core.symbolic.list.List | [
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|
41,007 | pyglove.core.symbolic.list | __deepcopy__ | null | def __deepcopy__(self, memo) -> 'List':
return self.sym_clone(deep=True, memo=memo)
| (self, memo) -> pyglove.core.symbolic.list.List | [
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|
41,008 | pyglove.core.symbolic.list | __delitem__ | Delete an item from the List. | def __delitem__(self, index: int) -> None:
"""Delete an item from the List."""
if base.treats_as_sealed(self):
raise base.WritePermissionError('Cannot delete item from a sealed List.')
if not base.writtable_via_accessors(self):
raise base.WritePermissionError(
self._error_message('Cannot delete List item while accessor_writable '
'is set to False. '
'Use \'rebind\' method instead.'))
if not isinstance(index, numbers.Integral):
raise TypeError(
f'list index must be an integer. Encountered {index!r}.')
if index < -len(self) or index >= len(self):
raise IndexError(
f'list index out of range. '
f'Length={len(self)}, index={index}')
old_value = self.sym_getattr(index)
super().__delitem__(index)
if flags.is_change_notification_enabled():
self._notify_field_updates([
base.FieldUpdate(
self.sym_path + index, self,
self._value_spec.element if self._value_spec else None,
old_value, pg_typing.MISSING_VALUE)
])
| (self, index: int) -> NoneType | [
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|
41,009 | pyglove.core.symbolic.list | __getitem__ | Gets the item at a given position. | def __getitem__(self, index) -> Any:
"""Gets the item at a given position."""
if isinstance(index, numbers.Integral):
if index < -len(self) or index >= len(self):
raise IndexError('list index out of range')
return self.sym_inferred(index)
elif isinstance(index, slice):
return [self[i] for i in range(*self._parse_slice(index))]
else:
raise TypeError(
f'list index must be an integer. Encountered {index!r}.')
| (self, index) -> Any | [
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|
41,010 | pyglove.core.symbolic.list | __getstate__ | Customizes pickle.dump. | def __getstate__(self) -> Any:
"""Customizes pickle.dump."""
return dict(value=list(self), kwargs=self._init_kwargs())
| (self) -> Any | [
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|
41,011 | pyglove.core.symbolic.list | __hash__ | Overriden hashing function. | def __hash__(self) -> int:
"""Overriden hashing function."""
return self.sym_hash()
| (self) -> int | [
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|
41,012 | pyglove.core.symbolic.list | __init__ | Constructor.
Args:
items: A optional iterable object as initial value for this list.
value_spec: Value spec that applies to this List.
onchange_callback: Callback when sub-tree has been modified.
allow_partial: Whether to allow unbound or partial fields. This takes
effect only when value_spec is not None.
accessor_writable: Whether to allow modification of this List using
accessors (operator[]).
sealed: Whether to seal this List after creation.
root_path: KeyPath of this List in its object tree.
| def __init__(
self,
items: Optional[Iterable[Any]] = None,
*,
value_spec: Optional[pg_typing.List] = None,
onchange_callback: Optional[Callable[
[Dict[object_utils.KeyPath, base.FieldUpdate]], None]] = None,
allow_partial: bool = False,
accessor_writable: bool = True,
sealed: bool = False,
root_path: Optional[object_utils.KeyPath] = None):
"""Constructor.
Args:
items: A optional iterable object as initial value for this list.
value_spec: Value spec that applies to this List.
onchange_callback: Callback when sub-tree has been modified.
allow_partial: Whether to allow unbound or partial fields. This takes
effect only when value_spec is not None.
accessor_writable: Whether to allow modification of this List using
accessors (operator[]).
sealed: Whether to seal this List after creation.
root_path: KeyPath of this List in its object tree.
"""
if value_spec and not isinstance(value_spec, pg_typing.List):
raise TypeError(
f'Argument \'value_spec\' must be a `pg.typing.List` object. '
f'Encountered {value_spec}.')
# We delay seal operation until items are filled.
base.Symbolic.__init__(
self,
allow_partial=allow_partial,
accessor_writable=accessor_writable,
sealed=False,
root_path=root_path)
self._value_spec = None
self._onchange_callback = None
list.__init__(self)
if items:
# Copy the symbolic form instead of evaluated form.
if isinstance(items, List):
items = items.sym_values()
for item in items:
self._set_item_without_permission_check(len(self), item)
if value_spec:
self.use_value_spec(value_spec, allow_partial)
# NOTE(daiyip): We set onchange callback at the end of init to avoid
# triggering during initialization.
self._onchange_callback = onchange_callback
self.seal(sealed)
| (self, items: Optional[Iterable[Any]] = None, *, value_spec: Optional[pyglove.core.typing.value_specs.List] = None, onchange_callback: Optional[Callable[[Dict[pyglove.core.object_utils.value_location.KeyPath, pyglove.core.symbolic.base.FieldUpdate]], NoneType]] = None, allow_partial: bool = False, accessor_writable: bool = True, sealed: bool = False, root_path: Optional[pyglove.core.object_utils.value_location.KeyPath] = None) | [
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|
41,013 | pyglove.core.symbolic.list | __iter__ | Iterates the list. | def __iter__(self):
"""Iterates the list."""
for i in range(len(self)):
yield self.sym_inferred(i)
| (self) | [
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|
41,014 | pyglove.core.symbolic.list | __mul__ | Returns a repeated Lit of self. | def __mul__(self, n: int) -> 'List':
"""Returns a repeated Lit of self."""
result = List()
for _ in range(n):
result.extend(self)
if self._value_spec is not None:
result.use_value_spec(self._value_spec)
return result
| (self, n: int) -> pyglove.core.symbolic.list.List | [
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|
41,015 | pyglove.core.symbolic.list | __rmul__ | Returns a repeated Lit of self. | def __rmul__(self, n: int) -> 'List':
"""Returns a repeated Lit of self."""
return self.__mul__(n)
| (self, n: int) -> pyglove.core.symbolic.list.List | [
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|
41,016 | pyglove.core.symbolic.list | __setitem__ | Set item in this List. | def __setitem__(self, index, value: Any) -> None:
"""Set item in this List."""
if base.treats_as_sealed(self):
raise base.WritePermissionError(
self._error_message('Cannot set item for a sealed List.'))
if not base.writtable_via_accessors(self):
raise base.WritePermissionError(
self._error_message('Cannot modify List item by __setitem__ while '
'accessor_writable is set to False. '
'Use \'rebind\' method instead.'))
if isinstance(index, slice):
start, stop, step = self._parse_slice(index)
replacements = [self._formalized_value(i, v) for i, v in enumerate(value)]
if step < 0:
replacements.reverse()
step = -step
slice_size = math.ceil((stop - start) * 1.0 / step)
if step == 1:
if slice_size < len(replacements):
for i in range(slice_size, len(replacements)):
replacements[i] = Insertion(replacements[i])
else:
replacements.extend(
[pg_typing.MISSING_VALUE
for _ in range(slice_size - len(replacements))])
elif slice_size != len(replacements):
raise ValueError(
f'attempt to assign sequence of size {len(replacements)} to '
f'extended slice of size {slice_size}')
updates = []
for i, r in enumerate(replacements):
update = self._set_item_without_permission_check(start + i * step, r)
if update is not None:
updates.append(update)
if flags.is_change_notification_enabled() and updates:
self._notify_field_updates(updates)
elif isinstance(index, numbers.Integral):
if index < -len(self) or index >= len(self):
raise IndexError(
f'list assignment index out of range. '
f'Length={len(self)}, index={index}')
update = self._set_item_without_permission_check(index, value)
if flags.is_change_notification_enabled() and update:
self._notify_field_updates([update])
else:
raise TypeError(
f'list assignment index must be an integer. Encountered {index!r}.')
| (self, index, value: Any) -> NoneType | [
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|
41,020 | pyglove.core.symbolic.list | _formalized_value | Get transformed (formal) value from user input. | def _formalized_value(self, idx: int, value: Any):
"""Get transformed (formal) value from user input."""
allow_partial = base.accepts_partial(self)
value = base.from_json(
value,
allow_partial=allow_partial,
root_path=object_utils.KeyPath(idx, self.sym_path))
if self._value_spec and flags.is_type_check_enabled():
value = self._value_spec.element.apply(
value,
allow_partial=allow_partial,
transform_fn=base.symbolic_transform_fn(self._allow_partial),
root_path=object_utils.KeyPath(idx, self.sym_path))
return self._relocate_if_symbolic(idx, value)
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|
41,024 | pyglove.core.symbolic.list | _on_change | On change event of List. | def _on_change(self,
field_updates: Dict[object_utils.KeyPath, base.FieldUpdate]):
"""On change event of List."""
# Do nothing for now to handle changes of List.
# NOTE(daiyip): Remove items that are MISSING_VALUES.
keys_to_remove = []
for i, item in self.sym_items():
if pg_typing.MISSING_VALUE == item:
keys_to_remove.append(i)
if keys_to_remove:
for i in reversed(keys_to_remove):
list.__delitem__(self, i)
# Update paths for children.
for idx, item in self.sym_items():
if isinstance(item, base.TopologyAware) and item.sym_path.key != idx:
item.sym_setpath(object_utils.KeyPath(idx, self.sym_path))
if self._onchange_callback is not None:
self._onchange_callback(field_updates)
| (self, field_updates: Dict[pyglove.core.object_utils.value_location.KeyPath, pyglove.core.symbolic.base.FieldUpdate]) | [
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|
41,025 | pyglove.core.symbolic.list | _parse_slice | null | def _parse_slice(self, index: slice) -> Tuple[int, int, int]:
start = index.start if index.start is not None else 0
start = max(-len(self), start)
start = min(len(self), start)
if start < 0:
start += len(self)
stop = index.stop if index.stop is not None else len(self)
stop = max(-len(self), stop)
stop = min(len(self), stop)
if stop < 0:
stop += len(self)
step = index.step if index.step is not None else 1
return start, stop, step
| (self, index: slice) -> Tuple[int, int, int] | [
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|
41,028 | pyglove.core.symbolic.list | _set_item_without_permission_check | Set or add an item without permission check. | def _set_item_without_permission_check( # pytype: disable=signature-mismatch # overriding-parameter-type-checks
self, key: int, value: Any) -> Optional[base.FieldUpdate]:
"""Set or add an item without permission check."""
assert isinstance(key, numbers.Integral), key
index = key
if index >= len(self):
# Appending MISSING_VALUE is considered no-op.
if value == pg_typing.MISSING_VALUE:
return None
index = len(self)
should_insert = False
if isinstance(value, Insertion):
should_insert = True
value = value.value
old_value = pg_typing.MISSING_VALUE
# Replace an existing value.
if index < len(self) and not should_insert:
old_value = list.__getitem__(self, index)
# Generates no update as old value is the same as the new value.
if old_value is value:
return None
new_value = self._formalized_value(index, value)
if index < len(self):
if should_insert:
list.insert(self, index, new_value)
else:
list.__setitem__(self, index, new_value)
# Detach old value from object tree.
if isinstance(old_value, base.TopologyAware):
old_value.sym_setparent(None)
else:
super().append(new_value)
return base.FieldUpdate(
self.sym_path + index, self,
self._value_spec.element if self._value_spec else None,
old_value, new_value)
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|
41,030 | pyglove.core.symbolic.list | _sym_clone | Override Symbolic._clone. | def _sym_clone(self, deep: bool, memo=None) -> 'List':
"""Override Symbolic._clone."""
source = []
for v in self.sym_values():
if deep or isinstance(v, base.Symbolic):
v = base.clone(v, deep, memo)
source.append(v)
return List(
source,
value_spec=self._value_spec,
allow_partial=self._allow_partial,
accessor_writable=self._accessor_writable,
# NOTE(daiyip): parent and root_path are reset to empty
# for copy object.
root_path=None)
| (self, deep: bool, memo=None) -> pyglove.core.symbolic.list.List | [
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|
41,031 | pyglove.core.symbolic.list | _sym_getattr | Gets symbolic attribute by index. | def _sym_getattr(self, key: int) -> Any: # pytype: disable=signature-mismatch # overriding-parameter-type-checks
"""Gets symbolic attribute by index."""
return super().__getitem__(key)
| (self, key: int) -> Any | [
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|
41,033 | pyglove.core.symbolic.list | _sym_missing | Returns missing fields. | def _sym_missing(self) -> Dict[Any, Any]:
"""Returns missing fields."""
missing = dict()
for idx, elem in self.sym_items():
if isinstance(elem, base.Symbolic):
missing_child = elem.sym_missing(flatten=False)
if missing_child:
missing[idx] = missing_child
return missing
| (self) -> Dict[Any, Any] | [
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|
41,034 | pyglove.core.symbolic.list | _sym_nondefault | Returns non-default values. | def _sym_nondefault(self) -> Dict[int, Any]:
"""Returns non-default values."""
non_defaults = dict()
for idx, elem in self.sym_items():
if isinstance(elem, base.Symbolic):
non_defaults_child = elem.non_default_values(flatten=False)
if non_defaults_child:
non_defaults[idx] = non_defaults_child
else:
non_defaults[idx] = elem
return non_defaults
| (self) -> Dict[int, Any] | [
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|
41,036 | pyglove.core.symbolic.list | _sym_rebind | Subclass specific rebind implementation. | def _sym_rebind(
self, path_value_pairs: typing.Dict[object_utils.KeyPath, Any]
) -> typing.List[base.FieldUpdate]:
"""Subclass specific rebind implementation."""
updates = []
# Apply the updates in reverse order, so the operated path will not alter
# from insertions and deletions.
path_value_pairs = sorted(
path_value_pairs.items(), key=lambda x: x[0], reverse=True)
for k, v in path_value_pairs:
update = self._set_item_of_current_tree(k, v)
if update is not None:
updates.append(update)
# Reverse the updates so the update is from the smallest number to
# the largest.
updates.reverse()
return updates
| (self, path_value_pairs: Dict[pyglove.core.object_utils.value_location.KeyPath, Any]) -> List[pyglove.core.symbolic.base.FieldUpdate] | [
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|
41,037 | pyglove.core.symbolic.list | _update_children_paths | Update children paths according to root_path of current node. | def _update_children_paths(
self,
old_path: object_utils.KeyPath,
new_path: object_utils.KeyPath) -> None:
"""Update children paths according to root_path of current node."""
del old_path
for idx, item in self.sym_items():
if isinstance(item, base.TopologyAware):
item.sym_setpath(object_utils.KeyPath(idx, new_path))
| (self, old_path: pyglove.core.object_utils.value_location.KeyPath, new_path: pyglove.core.object_utils.value_location.KeyPath) -> NoneType | [
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|
41,038 | pyglove.core.symbolic.list | append | Appends an item. | def append(self, value: Any) -> None:
"""Appends an item."""
if base.treats_as_sealed(self):
raise base.WritePermissionError('Cannot append element on a sealed List.')
if self.max_size is not None and len(self) >= self.max_size:
raise ValueError(f'List reached its max size {self.max_size}.')
update = self._set_item_without_permission_check(len(self), value)
if flags.is_change_notification_enabled() and update:
self._notify_field_updates([update])
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|
41,039 | pyglove.core.symbolic.list | clear | Clears the list. | def clear(self) -> None:
"""Clears the list."""
if base.treats_as_sealed(self):
raise base.WritePermissionError('Cannot clear a sealed List.')
if self._value_spec and self._value_spec.min_size > 0:
raise ValueError(
f'List cannot be cleared: min size is {self._value_spec.min_size}.')
super().clear()
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|
41,041 | pyglove.core.symbolic.list | copy | Shallow current list. | def copy(self) -> 'List':
"""Shallow current list."""
return List(super().copy(), value_spec=self._value_spec)
| (self) -> pyglove.core.symbolic.list.List | [
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|
41,042 | pyglove.core.symbolic.list | custom_apply | Implement pg.typing.CustomTyping interface.
Args:
path: KeyPath of current object.
value_spec: Origin value spec of the field.
allow_partial: Whether allow partial object to be created.
child_transform: Function to transform child node values in dict_obj into
their final values. Transform function is called on leaf nodes first,
then on their containers, recursively.
Returns:
A tuple (proceed_with_standard_apply, transformed value)
| def custom_apply(
self,
path: object_utils.KeyPath,
value_spec: pg_typing.ValueSpec,
allow_partial: bool,
child_transform: Optional[
Callable[[object_utils.KeyPath, pg_typing.Field, Any], Any]] = None
) -> Tuple[bool, 'List']:
"""Implement pg.typing.CustomTyping interface.
Args:
path: KeyPath of current object.
value_spec: Origin value spec of the field.
allow_partial: Whether allow partial object to be created.
child_transform: Function to transform child node values in dict_obj into
their final values. Transform function is called on leaf nodes first,
then on their containers, recursively.
Returns:
A tuple (proceed_with_standard_apply, transformed value)
"""
proceed_with_standard_apply = True
if self._value_spec:
if value_spec and not value_spec.is_compatible(self._value_spec):
raise ValueError(
object_utils.message_on_path(
f'List (spec={self._value_spec!r}) cannot be assigned to an '
f'incompatible field (spec={value_spec!r}).', path))
if self._allow_partial == allow_partial:
proceed_with_standard_apply = False
else:
self._allow_partial = allow_partial
elif isinstance(value_spec, pg_typing.List):
self._value_spec = value_spec
return (proceed_with_standard_apply, self)
| (self, path: pyglove.core.object_utils.value_location.KeyPath, value_spec: pyglove.core.typing.class_schema.ValueSpec, allow_partial: bool, child_transform: Optional[Callable[[pyglove.core.object_utils.value_location.KeyPath, pyglove.core.typing.class_schema.Field, Any], Any]] = None) -> Tuple[bool, pyglove.core.symbolic.list.List] | [
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|
41,043 | pyglove.core.symbolic.list | extend | null | def extend(self, other: Iterable[Any]) -> None:
# NOTE(daiyip): THIS IS A WORKAROUND FOR WORKING WITH PICKLE.
# `pg.List` is a subclass of `list`, therefore, when pickle loads a list,
# it tries to set the list values directly by calling `extend` without
# calling `pg.List.__init__` at the first place. As a result, an error will
# raise, which complains about that an attribute set up during `__init__` is
# not available. A mitigation to this issue is to detect such calls in
# `extend`, and simply do nothing as follows, which will give a chance to
# `pg.List.__getstate__` to deal with the restoration logic as an object
# (instead of a list).
if not hasattr(self, '_sym_parent'):
return
if base.treats_as_sealed(self):
raise base.WritePermissionError('Cannot extend a sealed List.')
# Extend on the symbolic form instead of the evaluated form.
iter_other = other.sym_values() if isinstance(other, List) else other
other = list(iter_other)
if self.max_size is not None and len(self) + len(other) > self.max_size:
raise ValueError(
f'Cannot extend List: the number of elements '
f'({len(self) + len(other)}) exceeds max size ({self.max_size}).')
updates = []
for v in other:
update = self._set_item_without_permission_check(len(self), v)
if update is not None:
updates.append(update)
if flags.is_change_notification_enabled() and updates:
self._notify_field_updates(updates)
| (self, other: Iterable[Any]) -> NoneType | [
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|
41,044 | pyglove.core.symbolic.list | format | Formats this List. | def format(
self,
compact: bool = False,
verbose: bool = True,
root_indent: int = 0,
*,
python_format: bool = False,
use_inferred: bool = False,
cls_name: Optional[str] = None,
bracket_type: object_utils.BracketType = object_utils.BracketType.SQUARE,
**kwargs) -> str:
"""Formats this List."""
def _indent(text, indent):
return ' ' * 2 * indent + text
cls_name = cls_name or ''
open_bracket, close_bracket = object_utils.bracket_chars(bracket_type)
s = [f'{cls_name}{open_bracket}']
if compact:
kv_strs = []
for idx, elem in self.sym_items():
if use_inferred and isinstance(elem, base.Inferential):
elem = self.sym_inferred(idx, default=elem)
v_str = object_utils.format(
elem, compact, verbose, root_indent + 1,
python_format=python_format, use_inferred=use_inferred, **kwargs)
if python_format:
kv_strs.append(v_str)
else:
kv_strs.append(f'{idx}: {v_str}')
s.append(', '.join(kv_strs))
s.append(close_bracket)
else:
if self:
for idx, elem in self.sym_items():
if use_inferred and isinstance(elem, base.Inferential):
elem = self.sym_inferred(idx, default=elem)
if idx == 0:
s.append('\n')
else:
s.append(',\n')
v_str = object_utils.format(
elem, compact, verbose, root_indent + 1,
python_format=python_format, use_inferred=use_inferred, **kwargs)
if python_format:
s.append(_indent(v_str, root_indent + 1))
else:
s.append(_indent(f'{idx} : {v_str}', root_indent + 1))
s.append('\n')
s.append(_indent(close_bracket, root_indent))
else:
s.append(close_bracket)
return ''.join(s)
| (self, compact: bool = False, verbose: bool = True, root_indent: int = 0, *, python_format: bool = False, use_inferred: bool = False, cls_name: Optional[str] = None, bracket_type: pyglove.core.object_utils.formatting.BracketType = <BracketType.SQUARE: 1>, **kwargs) -> str | [
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|
41,045 | pyglove.core.symbolic.list | insert | Inserts an item at a given position. | def insert(self, index: int, value: Any) -> None:
"""Inserts an item at a given position."""
if base.treats_as_sealed(self):
raise base.WritePermissionError(
'Cannot insert element into a sealed List.')
if self.max_size is not None and len(self) >= self.max_size:
raise ValueError(f'List reached its max size {self.max_size}.')
update = self._set_item_without_permission_check(
index, mark_as_insertion(value))
if flags.is_change_notification_enabled() and update:
self._notify_field_updates([update])
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|
41,049 | pyglove.core.symbolic.list | pop | Pop an item and return its value. | def pop(self, index: int = -1) -> Any:
"""Pop an item and return its value."""
if index < -len(self) or index >= len(self):
raise IndexError('pop index out of range')
index = (index + len(self)) % len(self)
value = self[index]
with flags.allow_writable_accessors(True):
del self[index]
return value
| (self, index: int = -1) -> Any | [
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|
41,051 | pyglove.core.symbolic.list | remove | Removes the first occurrence of the value. | def remove(self, value: Any) -> None:
"""Removes the first occurrence of the value."""
for i, item in self.sym_items():
if item == value:
if (self._value_spec and self._value_spec.min_size == len(self)):
raise ValueError(
f'Cannot remove item: min size ({self._value_spec.min_size}) '
f'is reached.')
del self[i]
return
raise ValueError(f'{value!r} not in list.')
| (self, value: Any) -> NoneType | [
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|
41,052 | pyglove.core.symbolic.list | reverse | Reverse the elements of the list in place. | def reverse(self) -> None:
"""Reverse the elements of the list in place."""
if base.treats_as_sealed(self):
raise base.WritePermissionError('Cannot reverse a sealed List.')
super().reverse()
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|
41,054 | pyglove.core.symbolic.list | seal | Seal or unseal current object from further modification. | def seal(self, sealed: bool = True) -> 'List':
"""Seal or unseal current object from further modification."""
if self.is_sealed == sealed:
return self
for elem in self.sym_values():
if isinstance(elem, base.Symbolic):
elem.seal(sealed)
super().seal(sealed)
return self
| (self, sealed: bool = True) -> pyglove.core.symbolic.list.List | [
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|
41,056 | pyglove.core.symbolic.list | sort | Sorts the items of the list in place.. | def sort(self, *, key=None, reverse=False) -> None:
"""Sorts the items of the list in place.."""
if base.treats_as_sealed(self):
raise base.WritePermissionError('Cannot sort a sealed List.')
super().sort(key=key, reverse=reverse)
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|
41,058 | pyglove.core.symbolic.list | sym_attr_field | Returns the field definition for a symbolic attribute. | def sym_attr_field(self, key: Union[str, int]) -> Optional[pg_typing.Field]:
"""Returns the field definition for a symbolic attribute."""
del key
if self._value_spec is None:
return None
return self._value_spec.element
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|
41,067 | pyglove.core.symbolic.list | sym_hasattr | Tests if a symbolic attribute exists. | def sym_hasattr(self, key: Union[str, int]) -> bool:
"""Tests if a symbolic attribute exists."""
return (isinstance(key, numbers.Integral)
and key >= -len(self) and key < len(self))
| (self, key: Union[str, int]) -> bool | [
0.06760568916797638,
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|
41,068 | pyglove.core.symbolic.list | sym_hash | Symbolically hashing. | def sym_hash(self) -> int:
"""Symbolically hashing."""
return base.sym_hash(
(self.__class__, tuple([base.sym_hash(e) for e in self.sym_values()]))
)
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|
41,071 | pyglove.core.symbolic.list | sym_items | Iterates the (key, value) pairs of symbolic attributes. | def sym_items(self) -> Iterator[Tuple[int, Any]]:
"""Iterates the (key, value) pairs of symbolic attributes."""
for i in range(len(self)):
yield (i, super().__getitem__(i))
| (self) -> Iterator[Tuple[int, Any]] | [
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|
41,072 | pyglove.core.symbolic.list | sym_jsonify | Converts current list to a list of plain Python objects. | def sym_jsonify(
self,
use_inferred: bool = False,
**kwargs) -> object_utils.JSONValueType:
"""Converts current list to a list of plain Python objects."""
def json_item(idx):
v = self.sym_getattr(idx)
if use_inferred and isinstance(v, base.Inferential):
v = self.sym_inferred(idx, default=v)
return base.to_json(v, use_inferred=use_inferred, **kwargs)
return [json_item(i) for i in range(len(self))]
| (self, use_inferred: bool = False, **kwargs) -> Union[int, float, bool, str, List[Any], Dict[str, Any]] | [
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|
41,073 | pyglove.core.symbolic.list | sym_keys | Symbolically iterates indices. | def sym_keys(self) -> Iterator[int]:
"""Symbolically iterates indices."""
for i in range(len(self)):
yield i
| (self) -> Iterator[int] | [
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|
41,081 | pyglove.core.symbolic.base | sym_setparent | Sets the parent of current node in the symbolic tree. | def sym_setparent(self, parent: 'Symbolic'):
"""Sets the parent of current node in the symbolic tree."""
self._set_raw_attr('_sym_parent', parent)
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|
41,083 | pyglove.core.symbolic.list | sym_values | Iterates the values of symbolic attributes. | def sym_values(self) -> Iterator[Any]:
"""Iterates the values of symbolic attributes."""
for i in range(len(self)):
yield super().__getitem__(i)
| (self) -> Iterator[Any] | [
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|
41,086 | pyglove.core.symbolic.list | use_value_spec | Applies a ``pg.List`` as the value spec for current list.
Args:
value_spec: A List ValueSpec to apply to this List.
If current List is schema-less (whose immediate members are not
validated against schema), and `value_spec` is not None, the value spec
will be applied to the List.
Or else if current List is already symbolic (whose immediate members
are under the constraint of a List value spec), and `value_spec` is
None, current List will become schema-less. However, the schema
constraints for non-immediate members will remain.
allow_partial: Whether allow partial dict based on the schema. This flag
will override allow_partial flag in __init__ for spec-less List.
Returns:
Self.
Raises:
ValueError: schema validation failed due to value error.
RuntimeError: List is already bound with another value_spec.
TypeError: type errors during validation.
KeyError: key errors during validation.
| def use_value_spec(self,
value_spec: Optional[pg_typing.List],
allow_partial: bool = False) -> 'List':
"""Applies a ``pg.List`` as the value spec for current list.
Args:
value_spec: A List ValueSpec to apply to this List.
If current List is schema-less (whose immediate members are not
validated against schema), and `value_spec` is not None, the value spec
will be applied to the List.
Or else if current List is already symbolic (whose immediate members
are under the constraint of a List value spec), and `value_spec` is
None, current List will become schema-less. However, the schema
constraints for non-immediate members will remain.
allow_partial: Whether allow partial dict based on the schema. This flag
will override allow_partial flag in __init__ for spec-less List.
Returns:
Self.
Raises:
ValueError: schema validation failed due to value error.
RuntimeError: List is already bound with another value_spec.
TypeError: type errors during validation.
KeyError: key errors during validation.
"""
if value_spec is None:
self._value_spec = None
self._accessor_writable = True
return self
if not isinstance(value_spec, pg_typing.List):
raise ValueError(
self._error_message(
f'Value spec for list must be a `pg.typing.List` object. '
f'Encountered: {value_spec!r}'))
if self._value_spec and self._value_spec != value_spec:
raise RuntimeError(
self._error_message(
f'List is already bound with a different value '
f'spec: {self._value_spec}. New value spec: {value_spec}.'))
self._allow_partial = allow_partial
if flags.is_type_check_enabled():
# NOTE(daiyip): self._value_spec will be set in List.custom_apply method
# called by spec.apply, thus we don't need to set the _value_spec
# explicitly.
value_spec.apply(
self,
allow_partial=base.accepts_partial(self),
child_transform=base.symbolic_transform_fn(self._allow_partial),
root_path=self.sym_path)
else:
self._value_spec = value_spec
return self
| (self, value_spec: Optional[pyglove.core.typing.value_specs.List], allow_partial: bool = False) -> pyglove.core.symbolic.list.List | [
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|
41,087 | pyglove.core.object_utils.common_traits | MaybePartial | Interface for classes whose instances can be partially constructed.
A ``MaybePartial`` object is an object whose ``__init__`` method can accept
``pg.MISSING_VALUE`` as its argument values. All symbolic types (see
:class:`pyglove.Symbolic`) implements this interface, as their symbolic
attributes can be partially filled.
Example::
d = pg.Dict(x=pg.MISSING_VALUE, y=1)
assert d.is_partial
assert 'x' in d.missing_values()
| class MaybePartial(metaclass=abc.ABCMeta):
"""Interface for classes whose instances can be partially constructed.
A ``MaybePartial`` object is an object whose ``__init__`` method can accept
``pg.MISSING_VALUE`` as its argument values. All symbolic types (see
:class:`pyglove.Symbolic`) implements this interface, as their symbolic
attributes can be partially filled.
Example::
d = pg.Dict(x=pg.MISSING_VALUE, y=1)
assert d.is_partial
assert 'x' in d.missing_values()
"""
@property
def is_partial(self) -> bool:
"""Returns True if this object is partial. Otherwise False.
An object is considered partial when any of its required fields is missing,
or at least one member is partial. The subclass can override this method
to provide a more efficient solution.
"""
return len(self.missing_values()) > 0 # pylint: disable=g-explicit-length-test
@abc.abstractmethod
def missing_values(self, flatten: bool = True) -> Dict[str, Any]: # pylint: disable=redefined-outer-name
"""Returns missing values from this object.
Args:
flatten: If True, convert nested structures into a flattened dict using
key path (delimited by '.' and '[]') as key.
Returns:
A dict of key to MISSING_VALUE.
"""
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|
41,088 | pyglove.core.object_utils.common_traits | missing_values | Returns missing values from this object.
Args:
flatten: If True, convert nested structures into a flattened dict using
key path (delimited by '.' and '[]') as key.
Returns:
A dict of key to MISSING_VALUE.
| @abc.abstractmethod
def missing_values(self, flatten: bool = True) -> Dict[str, Any]: # pylint: disable=redefined-outer-name
"""Returns missing values from this object.
Args:
flatten: If True, convert nested structures into a flattened dict using
key path (delimited by '.' and '[]') as key.
Returns:
A dict of key to MISSING_VALUE.
"""
| (self, flatten: bool = True) -> Dict[str, Any] | [
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|
41,089 | pyglove.core.symbolic.object | Object | Base class for symbolic user classes.
PyGlove allow symbolic programming interfaces to be easily added to most
Python classes in two ways:
* Developing a dataclass-like symbolic class by subclassing ``pg.Object``.
* Developing a class as usual and decorate it using :func:`pyglove.symbolize`.
This also work with existing classes.
By directly subclassing ``pg.Object``, programmers can create new symbolic
classes with the least effort. For example::
@pg.members([
# Each tuple in the list defines a symbolic field for `__init__`.
('name', pg.typing.Str().noneable(), 'Name to greet'),
('time_of_day',
pg.typing.Enum('morning', ['morning', 'afternnon', 'evening']),
'Time of the day.')
])
class Greeting(pg.Object):
def __call__(self):
# Values for symbolic fields can be accessed
# as public data members of the symbolic object.
print(f'Good {self.time_of_day}, {self.name}')
# Create an object of Greeting and invoke it,
# which shall print 'Good morning, Bob'.
Greeting('Bob')()
Symbolic fields can be inherited from the base symbolic class: the fields
from the base class will be copied to the subclass in their declaration
order, while the subclass can override the inherited fields with more
restricted validation rules or different default values. For example::
@pg.members([
('x', pg.typing.Int(max_value=10)),
('y', pg.typing.Float(min_value=0))
])
class Foo(pg.Object)
pass
@pg.members([
('x', pg.typing.Int(min_value=1, default=1)),
('z', pg.typing.Str().noneable())
])
class Bar(Foo)
pass
# Printing Bar's schema will show that there are 3 parameters defined:
# x : pg.typing.Int(min_value=1, max_value=10, default=1))
# y : pg.typing.Float(min_value=0)
# z : pg.typing.Str().noneable()
print(Bar.__schema__)
| class Object(base.Symbolic, metaclass=ObjectMeta):
"""Base class for symbolic user classes.
PyGlove allow symbolic programming interfaces to be easily added to most
Python classes in two ways:
* Developing a dataclass-like symbolic class by subclassing ``pg.Object``.
* Developing a class as usual and decorate it using :func:`pyglove.symbolize`.
This also work with existing classes.
By directly subclassing ``pg.Object``, programmers can create new symbolic
classes with the least effort. For example::
@pg.members([
# Each tuple in the list defines a symbolic field for `__init__`.
('name', pg.typing.Str().noneable(), 'Name to greet'),
('time_of_day',
pg.typing.Enum('morning', ['morning', 'afternnon', 'evening']),
'Time of the day.')
])
class Greeting(pg.Object):
def __call__(self):
# Values for symbolic fields can be accessed
# as public data members of the symbolic object.
print(f'Good {self.time_of_day}, {self.name}')
# Create an object of Greeting and invoke it,
# which shall print 'Good morning, Bob'.
Greeting('Bob')()
Symbolic fields can be inherited from the base symbolic class: the fields
from the base class will be copied to the subclass in their declaration
order, while the subclass can override the inherited fields with more
restricted validation rules or different default values. For example::
@pg.members([
('x', pg.typing.Int(max_value=10)),
('y', pg.typing.Float(min_value=0))
])
class Foo(pg.Object)
pass
@pg.members([
('x', pg.typing.Int(min_value=1, default=1)),
('z', pg.typing.Str().noneable())
])
class Bar(Foo)
pass
# Printing Bar's schema will show that there are 3 parameters defined:
# x : pg.typing.Int(min_value=1, max_value=10, default=1))
# y : pg.typing.Float(min_value=0)
# z : pg.typing.Str().noneable()
print(Bar.__schema__)
"""
# Disable pytype attribute checking.
_HAS_DYNAMIC_ATTRIBUTES = True
# Class property that indicates whether to allow attribute access on symbolic
# members.
allow_symbolic_attribute = True
# Class property that indicates whether to allow to set or rebind symbolic
# members by value assginment.
allow_symbolic_assignment = False
# Allow symbolic mutation using `rebind`.
allow_symbolic_mutation = True
# Class property that indicates whether to use `sym_eq` for `__eq__`,
# `sym_ne` for `__ne__`, and `sym_hash` for `__hash__`.
use_symbolic_comparison = True
# If True, symbolic fields will be inferred from class annotations.
# It's an alternative way of declaring symbolic fields other than
# `pg.members`.
#
# e.g.::
#
# class A(pg.Object):
# x: int
# y: str
#
# Please note that class attributes in UPPER_CASE or starting with '_' will
# not be considered as symbolic fields even if they have annotations.
infer_symbolic_fields_from_annotations = True
# Automatically infer schema during subclass creation time.
auto_schema = True
#
# Customizable class behaviors.
#
def __init_subclass__(cls, user_cls=None):
"""Initializes subclass.
`pg.Object` allows child classes to explicit call
`pg.Object.__init_subclass__` in their `__init_subclass__`, to bypass other
classes' `__init__subclass__` in multi-inheritance use cases.
Example:
class Subclass(pg.Object, UserClass):
def __init_subclass__(cls):
# This bypasses UserClass.__init_subclass__
pg.Object.__init_subclass__(cls)
Args:
user_cls: The source class that calls this class method.
"""
object_utils.ensure_explicit_method_override(
cls.__init__,
(
'`pg.Object.__init__` is a PyGlove managed method. For setting up '
'the class initialization logic, please override `_on_bound()` or '
'`_on_init()`. If you do have a need to override `__init__` and '
'know the implications, please decorate your overridden method '
'with `@pg.explicit_method_override`.'
))
# Set `__serialization_key__` before JSONConvertible.__init_subclass__
# is called.
setattr(cls, '__serialization_key__', cls.__type_name__)
super().__init_subclass__()
user_cls = user_cls or cls
if user_cls.auto_schema:
# Inherit schema from base classes that have schema
# in the ordered of inheritance.
# TODO(daiyip): size of base_schema_list can be reduced
# by looking at their inheritance chains.
base_schema_list = []
for base_cls in user_cls.__bases__:
base_schema = getattr(base_cls, '__schema__', None)
if isinstance(base_schema, pg_typing.Schema):
base_schema_list.append(base_schema)
new_fields = user_cls._infer_fields_from_annotations()
cls_schema = schema_utils.formalize_schema(
pg_typing.create_schema(
new_fields,
name=user_cls.__type_name__,
base_schema_list=base_schema_list,
allow_nonconst_keys=True,
metadata={},
)
)
# NOTE(daiyip): When new fields are added through class attributes.
# We invalidate `init_arg_list` so PyGlove could recompute it based
# on its schema during `apply_schema`. Otherwise, we inherit the
# `init_arg_list` from the base class.
# TODO(daiyip): detect new fields based on the differences from the base
# schema.
if new_fields:
cls_schema.metadata['init_arg_list'] = None
user_cls.apply_schema(cls_schema)
@classmethod
def _on_schema_update(cls):
"""Customizable trait: handling schema change."""
# Update the default value for each field after schema is updated. This is
# because that users may change a field's default value via class attribute.
cls._update_default_values_from_class_attributes() # pylint: disable=no-value-for-parameter
# Update all schema-based signatures.
cls._update_signatures_based_on_schema()
# Expose symbolic attributes as object attributes when being asked.
if cls.allow_symbolic_attribute:
cls._generate_sym_attributes()
@classmethod
def _update_signatures_based_on_schema(cls):
"""Customizable trait: updates method signatures upon schema change."""
if cls.__init__ is not Object.__init__ and not hasattr(
cls.__init__, '__sym_generated_init__'
):
# We only generate `__init__` from pg.Object subclass which does not
# override the `__init__` method.
# Functor and ClassWrapper override their `__init__` methods, therefore
# they need to synchronize the __init__ signature by themselves.
return
signature = pg_typing.Signature.from_schema(
cls.__schema__, cls.__module__, '__init__', f'{cls.__name__}.__init__'
)
pseudo_init = signature.make_function(['pass'])
# Create a new `__init__` that passes through all the arguments to
# in `pg.Object.__init__`. This is needed for each class to use different
# signature.
@object_utils.explicit_method_override
@functools.wraps(pseudo_init)
def _init(self, *args, **kwargs):
# We pass through the arguments to `Object.__init__` instead of
# `super()` since the parent class uses a generated `__init__` will
# be delegated to `Object.__init__` eventually. Therefore, directly
# calling `Object.__init__` is equivalent to calling `super().__init__`.
Object.__init__(self, *args, **kwargs)
setattr(_init, '__sym_generated_init__', True)
setattr(cls, '__init__', _init)
@classmethod
def _generate_sym_attributes(cls):
"""Customizable trait: logics for generating symbolic attributes.."""
for key, field in cls.__schema__.fields.items():
if isinstance(key, pg_typing.ConstStrKey):
attr_name = str(key)
attr_value = cls.__dict__.get(attr_name, pg_typing.MISSING_VALUE)
if attr_value == pg_typing.MISSING_VALUE or (
not inspect.isfunction(attr_value)
and not isinstance(attr_value, property)
) | (*args, allow_partial: bool = False, sealed: Optional[bool] = None, root_path: Optional[pyglove.core.object_utils.value_location.KeyPath] = None, explicit_init: bool = False, **kwargs) | [
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|
41,097 | pyglove.core.symbolic.object | __init__ | Create an Object instance.
Args:
*args: positional arguments.
allow_partial: If True, the object can be partial.
sealed: If True, seal the object from future modification (unless under
a `pg.seal(False)` context manager). If False, treat the object as
unsealed. If None, it's determined by `cls.allow_symbolic_mutation`.
root_path: The symbolic path for current object. By default it's None,
which indicates that newly constructed object does not have a parent.
explicit_init: Should set to `True` when `__init__` is called via
`pg.Object.__init__` instead of `super().__init__`.
**kwargs: key/value arguments that align with the schema. All required
keys in the schema must be specified, and values should be acceptable
according to their value spec.
Raises:
KeyError: When required key(s) are missing.
ValueError: When value(s) are not acceptable by their value spec.
| @object_utils.explicit_method_override
def __init__(
self,
*args,
allow_partial: bool = False,
sealed: Optional[bool] = None,
root_path: Optional[object_utils.KeyPath] = None,
explicit_init: bool = False,
**kwargs):
"""Create an Object instance.
Args:
*args: positional arguments.
allow_partial: If True, the object can be partial.
sealed: If True, seal the object from future modification (unless under
a `pg.seal(False)` context manager). If False, treat the object as
unsealed. If None, it's determined by `cls.allow_symbolic_mutation`.
root_path: The symbolic path for current object. By default it's None,
which indicates that newly constructed object does not have a parent.
explicit_init: Should set to `True` when `__init__` is called via
`pg.Object.__init__` instead of `super().__init__`.
**kwargs: key/value arguments that align with the schema. All required
keys in the schema must be specified, and values should be acceptable
according to their value spec.
Raises:
KeyError: When required key(s) are missing.
ValueError: When value(s) are not acceptable by their value spec.
"""
# Placeholder for Google-internal usage instrumentation.
if sealed is None:
sealed = not self.__class__.allow_symbolic_mutation
if not isinstance(allow_partial, bool):
raise TypeError(
f'Expect bool type for argument \'allow_partial\' in '
f'symbolic.Object.__init__ but encountered {allow_partial}.')
# We delay the seal attempt until members are all set.
super().__init__(
allow_partial=allow_partial,
accessor_writable=self.__class__.allow_symbolic_assignment,
sealed=sealed,
root_path=root_path,
init_super=not explicit_init)
# Fill field_args and init_args from **kwargs.
_, unmatched_keys = self.__class__.__schema__.resolve(list(kwargs.keys()))
if unmatched_keys:
arg_phrase = object_utils.auto_plural(len(unmatched_keys), 'argument')
keys_str = object_utils.comma_delimited_str(unmatched_keys)
raise TypeError(
f'{self.__class__.__name__}.__init__() got unexpected '
f'keyword {arg_phrase}: {keys_str}')
field_args = {}
# Fill field_args and init_args from *args.
init_arg_names = self.__class__.init_arg_list
if args:
if not self.__class__.__schema__.fields:
raise TypeError(f'{self.__class__.__name__}() takes no arguments.')
elif init_arg_names and init_arg_names[-1].startswith('*'):
vararg_name = init_arg_names[-1][1:]
vararg_field = self.__class__.__schema__.get_field(vararg_name)
assert vararg_field is not None
num_named_args = len(init_arg_names) - 1
field_args[vararg_name] = list(args[num_named_args:])
args = args[:num_named_args]
elif len(args) > len(init_arg_names):
arg_phrase = object_utils.auto_plural(len(init_arg_names), 'argument')
was_phrase = object_utils.auto_plural(len(args), 'was', 'were')
raise TypeError(
f'{self.__class__.__name__}.__init__() takes '
f'{len(init_arg_names)} positional {arg_phrase} but {len(args)} '
f'{was_phrase} given.')
for i, arg_value in enumerate(args):
arg_name = init_arg_names[i]
field_args[arg_name] = arg_value
for k, v in kwargs.items():
if k in field_args:
values_str = object_utils.comma_delimited_str([field_args[k], v])
raise TypeError(
f'{self.__class__.__name__}.__init__() got multiple values for '
f'argument \'{k}\': {values_str}.')
field_args[k] = v
# Check missing arguments when partial binding is disallowed.
if not base.accepts_partial(self):
missing_args = []
for field in self.__class__.__schema__.fields.values():
if (not field.value.has_default
and isinstance(field.key, pg_typing.ConstStrKey)
and field.key not in field_args):
missing_args.append(str(field.key))
if missing_args:
arg_phrase = object_utils.auto_plural(len(missing_args), 'argument')
keys_str = object_utils.comma_delimited_str(missing_args)
raise TypeError(
f'{self.__class__.__name__}.__init__() missing {len(missing_args)} '
f'required {arg_phrase}: {keys_str}.')
self._set_raw_attr(
'_sym_attributes',
pg_dict.Dict(
field_args,
value_spec=self.__class__.sym_fields,
allow_partial=allow_partial,
sealed=sealed,
# NOTE(daiyip): Accessor writable is honored by
# `Object.__setattr__` thus we could always make `_sym_attributes`
# accessor writable. This prevents a child object's attribute access
# from being changed when it's attached to a parent whose symbolic
# attributes could not be directly written.
accessor_writable=True,
root_path=root_path,
as_object_attributes_container=True,
),
)
self._sym_attributes.sym_setparent(self)
self._on_init()
self.seal(sealed)
| (self, *args, allow_partial: bool = False, sealed: Optional[bool] = None, root_path: Optional[pyglove.core.object_utils.value_location.KeyPath] = None, explicit_init: bool = False, **kwargs) | [
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|
41,162 | pyglove.core.patching.object_factory | ObjectFactory | A factory to create symbolic object from a base value and patches.
Args:
value_type: Type of return value.
base_value: An instance of `value_type`,
or a callable object that produces an instance of `value_type`,
or a string as the path to the serialized value.
patches: Optional patching rules. See :func:`patch` for details.
params_override: A rebind dict (or a JSON string as serialized rebind dict)
as an additional patch to the value,
Returns:
Value after applying `patchers` and `params_override` based on `base_value`.
| from pyglove.core.patching.object_factory import ObjectFactory
| null | [
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|
41,181 | pyglove.core.symbolic.functor | _call | null | def functor_class(
func: types.FunctionType,
args: Optional[List[Union[
Tuple[Tuple[str, pg_typing.KeySpec], pg_typing.ValueSpec, str],
Tuple[Tuple[str, pg_typing.KeySpec], pg_typing.ValueSpec, str, Any]]]
] = None, # pylint: disable=bad-continuation
returns: Optional[pg_typing.ValueSpec] = None,
base_class: Optional[Type[Functor]] = None,
*,
auto_doc: bool = False,
auto_typing: bool = False,
serialization_key: Optional[str] = None,
additional_keys: Optional[List[str]] = None,
add_to_registry: bool = False,
) -> Type[Functor]:
"""Returns a functor class from a function.
Args:
func: Function to be wrapped into a functor.
args: Symbolic args specification. `args` is a list of tuples, each
describes an argument from the input function. Each tuple is the format of
(<argumment-name>, <value-spec>, [description], [metadata-objects]).
`argument-name` - a `str` or `pg_typing.StrKey` object. When
`pg_typing.StrKey` is used, it describes the wildcard keyword argument.
`value-spec` - a `pg_typing.ValueSpec` object or equivalent, e.g.
primitive values which will be converted to ValueSpec implementation
according to its type and used as its default value. `description` - a
string to describe the agument. `metadata-objects` - an optional list of
any type, which can be used to generate code according to the schema.
There are notable rules in filling the `args`: 1) When `args` is None or
arguments from the function signature are missing from it, `schema.Field`
for these fields will be automatically generated and inserted into `args`.
That being said, every arguments in input function will have a
`schema.Field` counterpart in `Functor.__schema__.fields` sorted by the
declaration order of each argument in the function signature ( other than
the order in `args`). 2) Default argument values are specified along with
function definition as regular python functions, instead of being set at
`schema.Field` level. But validation rules can be set using `args` and
apply to argument values.
For example::
@pg.functor([('c', pg.typing.Int(min_value=0), 'Arg c')])
def foo(a, b, c=1, **kwargs):
return a + b + c + sum(kwargs.values())
assert foo.schema.fields() == [
pg.typing.Field('a', pg.Any(), 'Argument a'.),
pg.typing.Field('b', pg.Any(), 'Argument b'.),
pg.typing.Field('c', pg.typing.Int(), 'Arg c.),
pg.typing.Filed(
pg.typing.StrKey(), pg.Any(), 'Other arguments.')
]
# Prebind a=1, b=2, with default value c=1.
assert foo(1, 2)() == 4
returns: Optional schema specification for the return value.
base_class: Optional base class (derived from `symbolic.Functor`). If None,
returned type will inherit from `Functor` directly.
auto_doc: If True, the descriptions of argument fields will be inherited
from funciton docstr if they are not explicitly specified through
``args``.
auto_typing: If True, the value spec for constraining each argument will be
inferred from its annotation. Otherwise the value specs for all arguments
will be ``pg.typing.Any()``.
serialization_key: An optional string to be used as the serialization key
for the class during `sym_jsonify`. If None, `cls.__type_name__` will be
used. This is introduced for scenarios when we want to relocate a class,
before the downstream can recognize the new location, we need the class to
serialize it using previous key.
additional_keys: An optional list of strings as additional keys to
deserialize an object of the registered class. This can be useful when we
need to relocate or rename the registered class while being able to load
existing serialized JSON values.
add_to_registry: If True, the newly created functor class will be added to
the registry for deserialization.
Returns:
`symbolic.Functor` subclass that wraps input function.
Raises:
KeyError: names of symbolic arguments are not compatible with
function signature.
TypeError: types of symbolic arguments are not compatible with
function signature.
ValueError: default values of symbolic arguments are not compatible
with function signature.
"""
if not inspect.isfunction(func):
raise TypeError(f'{func!r} is not a function.')
class _Functor(base_class or Functor):
"""Functor wrapper for input function."""
# The schema for function-based Functor will be inferred from the function
# signature. Therefore we do not infer the schema automatically during class
# creation.
auto_schema = False
# Do not infer symbolic fields from annotations, since this functor is
# created from function definition which does not have class-level
# attributes.
infer_symbolic_fields_from_annotations = True
def _call(self, *args, **kwargs):
return func(*args, **kwargs)
cls = typing.cast(Type[Functor], _Functor)
cls.__name__ = func.__name__
cls.__qualname__ = func.__qualname__
cls.__module__ = getattr(func, '__module__', 'wrapper')
cls.__doc__ = func.__doc__
# Enable automatic registration for subclass.
cls.auto_register = True
# Apply function schema.
schema = schema_utils.function_schema(
func, args, returns, auto_doc=auto_doc, auto_typing=auto_typing)
cls.apply_schema(schema)
# Register functor class for deserialization if needed.
if add_to_registry:
cls.register_for_deserialization(serialization_key, additional_keys)
return cls
| (self, *args, **kwargs) | [
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|
41,242 | pyglove.core.symbolic.origin | Origin | Class that represents the origin of a symbolic value.
Origin is used for debugging the creation chain of a symbolic value, as
well as keeping track of the factory or builder in creational design patterns.
An `Origin` object records the source value, a string tag, and optional
stack information on where a symbolic value is created.
Built-in tags are '__init__', 'clone', 'deepclone' and 'return'.
Users can pass custom tags to the `sym_setorigin` method of a symbolic value
for tracking its source in their own scenarios.
When origin tracking is enabled by calling `pg.track_origin(True)`, the
`sym_setorigin` method of symbolic values will be automatically called during
object creation, cloning or being returned from a functor. The stack
information can be obtained by `origin.stack` or `origin.stacktrace`.
| class Origin(object_utils.Formattable):
"""Class that represents the origin of a symbolic value.
Origin is used for debugging the creation chain of a symbolic value, as
well as keeping track of the factory or builder in creational design patterns.
An `Origin` object records the source value, a string tag, and optional
stack information on where a symbolic value is created.
Built-in tags are '__init__', 'clone', 'deepclone' and 'return'.
Users can pass custom tags to the `sym_setorigin` method of a symbolic value
for tracking its source in their own scenarios.
When origin tracking is enabled by calling `pg.track_origin(True)`, the
`sym_setorigin` method of symbolic values will be automatically called during
object creation, cloning or being returned from a functor. The stack
information can be obtained by `origin.stack` or `origin.stacktrace`.
"""
def __init__(self,
source: Any,
tag: str,
stacktrace: Optional[bool] = None,
stacklimit: Optional[int] = None,
stacktop: int = -1):
"""Constructor.
Args:
source: Source value for the origin.
tag: A descriptive tag of the origin. Built-in tags are:
'__init__', 'clone', 'deepclone', 'return'. Users can manually
call `sym_setorigin` with custom tag value.
stacktrace: If True, enable stack trace for the origin. If None, enable
stack trace if `pg.tracek_origin()` is called. Otherwise stack trace is
disabled.
stacklimit: An optional integer to limit the stack depth. If None, it's
determined by the value passed to `pg.set_origin_stacktrace_limit`,
which is 10 by default.
stacktop: A negative integer to indicate the stack top among the stack
frames that we want to present to user, by default it's 2-level up from
the stack within current `sym_setorigin` call.
"""
if not isinstance(tag, str):
raise ValueError(f'`tag` must be a string. Encountered: {tag!r}.')
self._source = source
self._tag = tag
self._stack = None
self._stacktrace = None
if stacktrace is None:
stacktrace = flags.is_tracking_origin()
if stacklimit is None:
stacklimit = flags.get_origin_stacktrace_limit()
if stacktrace:
self._stack = traceback.extract_stack(limit=stacklimit - stacktop)
if stacktop < 0:
self._stack = self._stack[:stacktop]
@property
def source(self) -> Any:
"""Returns the source object."""
return self._source
@property
def root(self) -> 'Origin':
"""Returns the root source of the origin."""
current = self
while True:
parent = getattr(current.source, 'sym_origin', None)
if parent is None:
break
current = parent
return current
def history(
self,
condition: Optional[Callable[['Origin'], bool]] = None) -> List['Origin']:
"""Returns a history of origins with an optional filter.
Args:
condition: An optional callable object with signature
(origin) -> should_list. If None, all origins will be listed.
Returns:
A list of filtered origin from the earliest (root) to the most recent.
"""
condition = condition or (lambda o: True)
current = self
history = []
while current is not None:
if condition(current):
history.append(current)
current = getattr(current.source, 'sym_origin', None)
history.reverse()
return history
@property
def tag(self) -> str:
"""Returns tag."""
return self._tag
@property
def stack(self) -> Optional[List[traceback.FrameSummary]]:
"""Returns the frame summary of original stack."""
return self._stack
@property
def stacktrace(self) -> Optional[str]:
"""Returns stack trace string."""
if self._stack is None:
return None
if self._stacktrace is None:
self._stacktrace = ''.join(traceback.format_list(self._stack))
return self._stacktrace
def chain(self, tag: Optional[str] = None) -> List['Origin']:
"""Get the origin list from the neareast to the farthest filtered by tag."""
origins = []
o = self
while o is not None:
if tag is None or tag == o.tag:
origins.append(o)
o = getattr(o.source, 'sym_origin', None)
return origins
def format(self,
compact: bool = False,
verbose: bool = True,
root_indent: int = 0,
**kwargs) -> str:
"""Formats this object."""
if isinstance(self._source, (str, type(None))):
source_str = object_utils.quote_if_str(self._source)
else:
source_info = object_utils.format(
self._source, compact, verbose, root_indent + 1, **kwargs)
source_str = f'{source_info} at 0x{id(self._source):8x}'
details = object_utils.kvlist_str([
('tag', object_utils.quote_if_str(self._tag), None),
('source', source_str, None),
])
return f'{self.__class__.__name__}({details})'
def __eq__(self, other: Any) -> bool:
"""Operator ==."""
if not isinstance(other, self.__class__):
return False
return self._source is other.source and self._tag == other.tag
def __ne__(self, other: Any) -> bool:
"""Operator !=."""
return not self.__eq__(other)
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|
41,243 | pyglove.core.symbolic.origin | __eq__ | Operator ==. | def __eq__(self, other: Any) -> bool:
"""Operator ==."""
if not isinstance(other, self.__class__):
return False
return self._source is other.source and self._tag == other.tag
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|
41,244 | pyglove.core.symbolic.origin | __init__ | Constructor.
Args:
source: Source value for the origin.
tag: A descriptive tag of the origin. Built-in tags are:
'__init__', 'clone', 'deepclone', 'return'. Users can manually
call `sym_setorigin` with custom tag value.
stacktrace: If True, enable stack trace for the origin. If None, enable
stack trace if `pg.tracek_origin()` is called. Otherwise stack trace is
disabled.
stacklimit: An optional integer to limit the stack depth. If None, it's
determined by the value passed to `pg.set_origin_stacktrace_limit`,
which is 10 by default.
stacktop: A negative integer to indicate the stack top among the stack
frames that we want to present to user, by default it's 2-level up from
the stack within current `sym_setorigin` call.
| def __init__(self,
source: Any,
tag: str,
stacktrace: Optional[bool] = None,
stacklimit: Optional[int] = None,
stacktop: int = -1):
"""Constructor.
Args:
source: Source value for the origin.
tag: A descriptive tag of the origin. Built-in tags are:
'__init__', 'clone', 'deepclone', 'return'. Users can manually
call `sym_setorigin` with custom tag value.
stacktrace: If True, enable stack trace for the origin. If None, enable
stack trace if `pg.tracek_origin()` is called. Otherwise stack trace is
disabled.
stacklimit: An optional integer to limit the stack depth. If None, it's
determined by the value passed to `pg.set_origin_stacktrace_limit`,
which is 10 by default.
stacktop: A negative integer to indicate the stack top among the stack
frames that we want to present to user, by default it's 2-level up from
the stack within current `sym_setorigin` call.
"""
if not isinstance(tag, str):
raise ValueError(f'`tag` must be a string. Encountered: {tag!r}.')
self._source = source
self._tag = tag
self._stack = None
self._stacktrace = None
if stacktrace is None:
stacktrace = flags.is_tracking_origin()
if stacklimit is None:
stacklimit = flags.get_origin_stacktrace_limit()
if stacktrace:
self._stack = traceback.extract_stack(limit=stacklimit - stacktop)
if stacktop < 0:
self._stack = self._stack[:stacktop]
| (self, source: Any, tag: str, stacktrace: Optional[bool] = None, stacklimit: Optional[int] = None, stacktop: int = -1) | [
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|
41,249 | pyglove.core.symbolic.origin | chain | Get the origin list from the neareast to the farthest filtered by tag. | def chain(self, tag: Optional[str] = None) -> List['Origin']:
"""Get the origin list from the neareast to the farthest filtered by tag."""
origins = []
o = self
while o is not None:
if tag is None or tag == o.tag:
origins.append(o)
o = getattr(o.source, 'sym_origin', None)
return origins
| (self, tag: Optional[str] = None) -> List[pyglove.core.symbolic.origin.Origin] | [
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|
41,250 | pyglove.core.symbolic.origin | format | Formats this object. | def format(self,
compact: bool = False,
verbose: bool = True,
root_indent: int = 0,
**kwargs) -> str:
"""Formats this object."""
if isinstance(self._source, (str, type(None))):
source_str = object_utils.quote_if_str(self._source)
else:
source_info = object_utils.format(
self._source, compact, verbose, root_indent + 1, **kwargs)
source_str = f'{source_info} at 0x{id(self._source):8x}'
details = object_utils.kvlist_str([
('tag', object_utils.quote_if_str(self._tag), None),
('source', source_str, None),
])
return f'{self.__class__.__name__}({details})'
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|
41,251 | pyglove.core.symbolic.origin | history | Returns a history of origins with an optional filter.
Args:
condition: An optional callable object with signature
(origin) -> should_list. If None, all origins will be listed.
Returns:
A list of filtered origin from the earliest (root) to the most recent.
| def history(
self,
condition: Optional[Callable[['Origin'], bool]] = None) -> List['Origin']:
"""Returns a history of origins with an optional filter.
Args:
condition: An optional callable object with signature
(origin) -> should_list. If None, all origins will be listed.
Returns:
A list of filtered origin from the earliest (root) to the most recent.
"""
condition = condition or (lambda o: True)
current = self
history = []
while current is not None:
if condition(current):
history.append(current)
current = getattr(current.source, 'sym_origin', None)
history.reverse()
return history
| (self, condition: Optional[Callable[[pyglove.core.symbolic.origin.Origin], bool]] = None) -> List[pyglove.core.symbolic.origin.Origin] | [
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|
41,252 | pyglove.core.symbolic.pure_symbolic | PureSymbolic | Base class to classes whose objects are considered pure symbolic.
Pure symbolic objects can be used for representing abstract concepts - for
example, a search space of objects - which cannot be executed but soely
representational.
Having pure symbolic object is a key differentiator of symbolic OOP from
regular OOP, which can be used to placehold values in an object as a
high-level expression of ideas. Later, with symbolic manipulation, the
pure symbolic objects are replaced with material values so the object
can be evaluated. This effectively decouples the expression of ideas from
the implementation of ideas. For example: ``pg.oneof(['a', 'b', 'c']`` will
be manipulated into 'a', 'b' or 'c' based on the decision of a search
algorithm, letting the program evolve itself.
| class PureSymbolic(pg_typing.CustomTyping):
"""Base class to classes whose objects are considered pure symbolic.
Pure symbolic objects can be used for representing abstract concepts - for
example, a search space of objects - which cannot be executed but soely
representational.
Having pure symbolic object is a key differentiator of symbolic OOP from
regular OOP, which can be used to placehold values in an object as a
high-level expression of ideas. Later, with symbolic manipulation, the
pure symbolic objects are replaced with material values so the object
can be evaluated. This effectively decouples the expression of ideas from
the implementation of ideas. For example: ``pg.oneof(['a', 'b', 'c']`` will
be manipulated into 'a', 'b' or 'c' based on the decision of a search
algorithm, letting the program evolve itself.
"""
def custom_apply(
self,
path: object_utils.KeyPath,
value_spec: pg_typing.ValueSpec,
allow_partial: bool,
child_transform: Optional[
Callable[[object_utils.KeyPath, pg_typing.Field, Any], Any]] = None
) -> Tuple[bool, Any]:
"""Custom apply on a value based on its original value spec.
This implements ``pg.pg_typing.CustomTyping``, allowing a pure symbolic
value to be assigned to any field. To customize this behavior, override
this method in subclasses.
Args:
path: KeyPath of current object under its object tree.
value_spec: Original value spec for this field.
allow_partial: Whether allow partial object to be created.
child_transform: Function to transform child node values into their final
values. Transform function is called on leaf nodes first, then on their
parents, recursively.
Returns:
A tuple (proceed_with_standard_apply, value_to_proceed).
If proceed_with_standard_apply is set to False, value_to_proceed
will be used as final value.
Raises:
Error when the value is not compatible with the value spec.
"""
del path, value_spec, allow_partial, child_transform
return (False, self)
| () | [
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|
41,254 | pyglove.core.symbolic.ref | Ref | Symbolic reference.
When adding a symbolic node to a symbolic tree, it undergoes a copy operation
if it already has a parent, ensuring that all symbolic objects have a single
parent. Additionally, list and dict objects are automatically converted to
``pg.List`` and ``pg.Dict``, respectively, to enable symbolic operability.
However, these two conventions come with certain costs. The act of making
copies incurs a runtime cost, and it also introduces challenges in sharing
states across different symbolic objects. To address this issue, symbolic
reference is introduced. This feature allows a symbolic node to refer to
value objects without the need for transformation or copying, even when the
symbolic node itself is copied. For example::
class A(pg.Object):
x: int
a = pg.Ref(A(1))
b = pg.Dict(x=a)
c = pg.Dict(y=a)
assert b.x is a
assert c.y is a
assert b.clone().x is a
assert c.clone(deep=True).y is a
In this example, ``pg.Ref`` is used to create a symbolic reference to the
object ``A(1)``, and the ``pg.Dict`` objects `b` and `c` can then reference
`a` without creating additional copies. This mechanism not only mitigates
the runtime cost but also facilitates seamless sharing of states among various
symbolic objects.
Another useful scenario arises when we wish to utilize regular Python list
and dict objects. In this case, ``pg.Ref`` enables us to access the list/dict
object as fields in the symbolic tree without requiring them to be transformed
into ``pg.List`` and ``pg.Dict``. This allows for seamless integration of
standard Python containers within the symbolic structure::
d = pg.Dict(x=pg.Ref({1: 2}))
assert isinstance(d.x, dict)
assert not isinstance(d.x, pg.Dict)
e = pg.Dict(x=pg.Ref([0, 1, 2]]))
assert isinstance(e.x, list)
assert not isinstance(e.x, pg.List)
Please be aware that ``pg.Ref`` objects are treated as leaf nodes in the
symbolic tree, even when they reference other symbolic objects. As a result,
the ``rebind()`` method cannot modify the value they are pointing to.
For primitive types, ``pg.Ref()`` returns their values directly without
creating a reference. For example, ``pg.Ref(1)`` and ``pg.Ref('abc')`` will
simply return the values 1 and 'abc', respectively, without any additional
referencing.
| class Ref(Object, base.Inferential):
"""Symbolic reference.
When adding a symbolic node to a symbolic tree, it undergoes a copy operation
if it already has a parent, ensuring that all symbolic objects have a single
parent. Additionally, list and dict objects are automatically converted to
``pg.List`` and ``pg.Dict``, respectively, to enable symbolic operability.
However, these two conventions come with certain costs. The act of making
copies incurs a runtime cost, and it also introduces challenges in sharing
states across different symbolic objects. To address this issue, symbolic
reference is introduced. This feature allows a symbolic node to refer to
value objects without the need for transformation or copying, even when the
symbolic node itself is copied. For example::
class A(pg.Object):
x: int
a = pg.Ref(A(1))
b = pg.Dict(x=a)
c = pg.Dict(y=a)
assert b.x is a
assert c.y is a
assert b.clone().x is a
assert c.clone(deep=True).y is a
In this example, ``pg.Ref`` is used to create a symbolic reference to the
object ``A(1)``, and the ``pg.Dict`` objects `b` and `c` can then reference
`a` without creating additional copies. This mechanism not only mitigates
the runtime cost but also facilitates seamless sharing of states among various
symbolic objects.
Another useful scenario arises when we wish to utilize regular Python list
and dict objects. In this case, ``pg.Ref`` enables us to access the list/dict
object as fields in the symbolic tree without requiring them to be transformed
into ``pg.List`` and ``pg.Dict``. This allows for seamless integration of
standard Python containers within the symbolic structure::
d = pg.Dict(x=pg.Ref({1: 2}))
assert isinstance(d.x, dict)
assert not isinstance(d.x, pg.Dict)
e = pg.Dict(x=pg.Ref([0, 1, 2]]))
assert isinstance(e.x, list)
assert not isinstance(e.x, pg.List)
Please be aware that ``pg.Ref`` objects are treated as leaf nodes in the
symbolic tree, even when they reference other symbolic objects. As a result,
the ``rebind()`` method cannot modify the value they are pointing to.
For primitive types, ``pg.Ref()`` returns their values directly without
creating a reference. For example, ``pg.Ref(1)`` and ``pg.Ref('abc')`` will
simply return the values 1 and 'abc', respectively, without any additional
referencing.
"""
def __new__(cls, value: Any, **kwargs):
del kwargs
if isinstance(value, (bool, numbers.Number, str)):
return value
return object.__new__(cls)
@object_utils.explicit_method_override
def __init__(self, value: Any, **kwargs) -> None:
super().__init__(**kwargs)
if isinstance(value, Ref):
value = value.value
self._value = value
def _on_parent_change(
self,
old_parent: Optional[base.Symbolic],
new_parent: Optional[base.Symbolic]) -> None:
if (new_parent is not None
and isinstance(self._value, base.Symbolic)
and self._value.sym_root is new_parent.sym_root):
raise NotImplementedError('Self-referential object is not supported.')
@property
def value(self) -> Any:
"""Returns the referenced value."""
return self._value
def infer(self, **kwargs) -> Any:
"""Returns the referenced value."""
return self._value
def custom_apply(
self,
path: object_utils.KeyPath,
value_spec: pg_typing.ValueSpec,
allow_partial: bool = False,
child_transform: Optional[Callable[
[object_utils.KeyPath, pg_typing.Field, Any], Any]] = None
) -> Tuple[bool, Any]:
"""Validate candidates during value_spec binding time."""
del child_transform
# Check if the field being assigned could accept the referenced value.
# We do not do any transformation, thus not passing the child transform.
value_spec.apply(
self._value,
allow_partial=allow_partial)
return (False, self)
def _sym_clone(self, deep: bool, memo: Any = None) -> 'Ref':
# Always create a new object.
# TODO(daiyip): support deep clone with the update of reference when
# the original value is updated.
return Ref(self._value, allow_partial=self.allow_partial)
def sym_eq(self, other: Any) -> bool:
return isinstance(other, Ref) and self.value is other.value
def sym_jsonify(self, **kwargs: Any) -> Any:
raise TypeError(f'{self!r} cannot be serialized at the moment.')
def __getstate__(self):
raise TypeError(f'{self!r} cannot be pickled at the moment.')
def format(
self,
compact: bool = False,
verbose: bool = False,
root_indent: int = 0,
**kwargs: Any) -> str:
value_str = object_utils.format(
self._value,
compact=compact, verbose=verbose, root_indent=root_indent + 1)
if compact:
return f'{self.__class__.__name__}({value_str})'
else:
return (f'{self.__class__.__name__}(\n'
+ ' ' * (root_indent + 1)
+ f'value = {value_str}\n'
+ ' ' * root_indent + ')')
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|
41,260 | pyglove.core.symbolic.ref | __getstate__ | null | def __getstate__(self):
raise TypeError(f'{self!r} cannot be pickled at the moment.')
| (self) | [
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|
41,262 | pyglove.core.symbolic.ref | __init__ | null | @object_utils.explicit_method_override
def __init__(self, value: Any, **kwargs) -> None:
super().__init__(**kwargs)
if isinstance(value, Ref):
value = value.value
self._value = value
| (self, value: Any, **kwargs) -> NoneType | [
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|
41,264 | pyglove.core.symbolic.ref | __new__ | null | def __new__(cls, value: Any, **kwargs):
del kwargs
if isinstance(value, (bool, numbers.Number, str)):
return value
return object.__new__(cls)
| (cls, value: Any, **kwargs) | [
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|
41,276 | pyglove.core.symbolic.ref | _on_parent_change | null | def _on_parent_change(
self,
old_parent: Optional[base.Symbolic],
new_parent: Optional[base.Symbolic]) -> None:
if (new_parent is not None
and isinstance(self._value, base.Symbolic)
and self._value.sym_root is new_parent.sym_root):
raise NotImplementedError('Self-referential object is not supported.')
| (self, old_parent: Optional[pyglove.core.symbolic.base.Symbolic], new_parent: Optional[pyglove.core.symbolic.base.Symbolic]) -> NoneType | [
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|
41,282 | pyglove.core.symbolic.ref | _sym_clone | null | def _sym_clone(self, deep: bool, memo: Any = None) -> 'Ref':
# Always create a new object.
# TODO(daiyip): support deep clone with the update of reference when
# the original value is updated.
return Ref(self._value, allow_partial=self.allow_partial)
| (self, deep: bool, memo: Optional[Any] = None) -> pyglove.core.symbolic.ref.Ref | [
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|
41,291 | pyglove.core.symbolic.ref | custom_apply | Validate candidates during value_spec binding time. | def custom_apply(
self,
path: object_utils.KeyPath,
value_spec: pg_typing.ValueSpec,
allow_partial: bool = False,
child_transform: Optional[Callable[
[object_utils.KeyPath, pg_typing.Field, Any], Any]] = None
) -> Tuple[bool, Any]:
"""Validate candidates during value_spec binding time."""
del child_transform
# Check if the field being assigned could accept the referenced value.
# We do not do any transformation, thus not passing the child transform.
value_spec.apply(
self._value,
allow_partial=allow_partial)
return (False, self)
| (self, path: pyglove.core.object_utils.value_location.KeyPath, value_spec: pyglove.core.typing.class_schema.ValueSpec, allow_partial: bool = False, child_transform: Optional[Callable[[pyglove.core.object_utils.value_location.KeyPath, pyglove.core.typing.class_schema.Field, Any], Any]] = None) -> Tuple[bool, Any] | [
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|
41,292 | pyglove.core.symbolic.ref | format | null | def format(
self,
compact: bool = False,
verbose: bool = False,
root_indent: int = 0,
**kwargs: Any) -> str:
value_str = object_utils.format(
self._value,
compact=compact, verbose=verbose, root_indent=root_indent + 1)
if compact:
return f'{self.__class__.__name__}({value_str})'
else:
return (f'{self.__class__.__name__}(\n'
+ ' ' * (root_indent + 1)
+ f'value = {value_str}\n'
+ ' ' * root_indent + ')')
| (self, compact: bool = False, verbose: bool = False, root_indent: int = 0, **kwargs: Any) -> str | [
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|
41,293 | pyglove.core.symbolic.ref | infer | Returns the referenced value. | def infer(self, **kwargs) -> Any:
"""Returns the referenced value."""
return self._value
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|
41,306 | pyglove.core.symbolic.ref | sym_eq | null | def sym_eq(self, other: Any) -> bool:
return isinstance(other, Ref) and self.value is other.value
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|
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