Current File : //opt/cloudlinux/venv/lib/python3.11/site-packages/pydantic/v1/utils.py
import keyword
import warnings
import weakref
from collections import OrderedDict, defaultdict, deque
from copy import deepcopy
from itertools import islice, zip_longest
from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType
from typing import (
TYPE_CHECKING,
AbstractSet,
Any,
Callable,
Collection,
Dict,
Generator,
Iterable,
Iterator,
List,
Mapping,
NoReturn,
Optional,
Set,
Tuple,
Type,
TypeVar,
Union,
)
from typing_extensions import Annotated
from .errors import ConfigError
from .typing import (
NoneType,
WithArgsTypes,
all_literal_values,
display_as_type,
get_args,
get_origin,
is_literal_type,
is_union,
)
from .version import version_info
if TYPE_CHECKING:
from inspect import Signature
from pathlib import Path
from .config import BaseConfig
from .dataclasses import Dataclass
from .fields import ModelField
from .main import BaseModel
from .typing import AbstractSetIntStr, DictIntStrAny, IntStr, MappingIntStrAny, ReprArgs
RichReprResult = Iterable[Union[Any, Tuple[Any], Tuple[str, Any], Tuple[str, Any, Any]]]
__all__ = (
'import_string',
'sequence_like',
'validate_field_name',
'lenient_isinstance',
'lenient_issubclass',
'in_ipython',
'is_valid_identifier',
'deep_update',
'update_not_none',
'almost_equal_floats',
'get_model',
'to_camel',
'is_valid_field',
'smart_deepcopy',
'PyObjectStr',
'Representation',
'GetterDict',
'ValueItems',
'version_info', # required here to match behaviour in v1.3
'ClassAttribute',
'path_type',
'ROOT_KEY',
'get_unique_discriminator_alias',
'get_discriminator_alias_and_values',
'DUNDER_ATTRIBUTES',
)
ROOT_KEY = '__root__'
# these are types that are returned unchanged by deepcopy
IMMUTABLE_NON_COLLECTIONS_TYPES: Set[Type[Any]] = {
int,
float,
complex,
str,
bool,
bytes,
type,
NoneType,
FunctionType,
BuiltinFunctionType,
LambdaType,
weakref.ref,
CodeType,
# note: including ModuleType will differ from behaviour of deepcopy by not producing error.
# It might be not a good idea in general, but considering that this function used only internally
# against default values of fields, this will allow to actually have a field with module as default value
ModuleType,
NotImplemented.__class__,
Ellipsis.__class__,
}
# these are types that if empty, might be copied with simple copy() instead of deepcopy()
BUILTIN_COLLECTIONS: Set[Type[Any]] = {
list,
set,
tuple,
frozenset,
dict,
OrderedDict,
defaultdict,
deque,
}
def import_string(dotted_path: str) -> Any:
"""
Stolen approximately from django. Import a dotted module path and return the attribute/class designated by the
last name in the path. Raise ImportError if the import fails.
"""
from importlib import import_module
try:
module_path, class_name = dotted_path.strip(' ').rsplit('.', 1)
except ValueError as e:
raise ImportError(f'"{dotted_path}" doesn\'t look like a module path') from e
module = import_module(module_path)
try:
return getattr(module, class_name)
except AttributeError as e:
raise ImportError(f'Module "{module_path}" does not define a "{class_name}" attribute') from e
def truncate(v: Union[str], *, max_len: int = 80) -> str:
"""
Truncate a value and add a unicode ellipsis (three dots) to the end if it was too long
"""
warnings.warn('`truncate` is no-longer used by pydantic and is deprecated', DeprecationWarning)
if isinstance(v, str) and len(v) > (max_len - 2):
# -3 so quote + string + … + quote has correct length
return (v[: (max_len - 3)] + '…').__repr__()
try:
v = v.__repr__()
except TypeError:
v = v.__class__.__repr__(v) # in case v is a type
if len(v) > max_len:
v = v[: max_len - 1] + '…'
return v
def sequence_like(v: Any) -> bool:
return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque))
def validate_field_name(bases: List[Type['BaseModel']], field_name: str) -> None:
"""
Ensure that the field's name does not shadow an existing attribute of the model.
"""
for base in bases:
if getattr(base, field_name, None):
raise NameError(
f'Field name "{field_name}" shadows a BaseModel attribute; '
f'use a different field name with "alias=\'{field_name}\'".'
)
def lenient_isinstance(o: Any, class_or_tuple: Union[Type[Any], Tuple[Type[Any], ...], None]) -> bool:
try:
return isinstance(o, class_or_tuple) # type: ignore[arg-type]
except TypeError:
return False
def lenient_issubclass(cls: Any, class_or_tuple: Union[Type[Any], Tuple[Type[Any], ...], None]) -> bool:
try:
return isinstance(cls, type) and issubclass(cls, class_or_tuple) # type: ignore[arg-type]
except TypeError:
if isinstance(cls, WithArgsTypes):
return False
raise # pragma: no cover
def in_ipython() -> bool:
"""
Check whether we're in an ipython environment, including jupyter notebooks.
"""
try:
eval('__IPYTHON__')
except NameError:
return False
else: # pragma: no cover
return True
def is_valid_identifier(identifier: str) -> bool:
"""
Checks that a string is a valid identifier and not a Python keyword.
:param identifier: The identifier to test.
:return: True if the identifier is valid.
"""
return identifier.isidentifier() and not keyword.iskeyword(identifier)
KeyType = TypeVar('KeyType')
def deep_update(mapping: Dict[KeyType, Any], *updating_mappings: Dict[KeyType, Any]) -> Dict[KeyType, Any]:
updated_mapping = mapping.copy()
for updating_mapping in updating_mappings:
for k, v in updating_mapping.items():
if k in updated_mapping and isinstance(updated_mapping[k], dict) and isinstance(v, dict):
updated_mapping[k] = deep_update(updated_mapping[k], v)
else:
updated_mapping[k] = v
return updated_mapping
def update_not_none(mapping: Dict[Any, Any], **update: Any) -> None:
mapping.update({k: v for k, v in update.items() if v is not None})
def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:
"""
Return True if two floats are almost equal
"""
return abs(value_1 - value_2) <= delta
def generate_model_signature(
init: Callable[..., None], fields: Dict[str, 'ModelField'], config: Type['BaseConfig']
) -> 'Signature':
"""
Generate signature for model based on its fields
"""
from inspect import Parameter, Signature, signature
from .config import Extra
present_params = signature(init).parameters.values()
merged_params: Dict[str, Parameter] = {}
var_kw = None
use_var_kw = False
for param in islice(present_params, 1, None): # skip self arg
if param.kind is param.VAR_KEYWORD:
var_kw = param
continue
merged_params[param.name] = param
if var_kw: # if custom init has no var_kw, fields which are not declared in it cannot be passed through
allow_names = config.allow_population_by_field_name
for field_name, field in fields.items():
param_name = field.alias
if field_name in merged_params or param_name in merged_params:
continue
elif not is_valid_identifier(param_name):
if allow_names and is_valid_identifier(field_name):
param_name = field_name
else:
use_var_kw = True
continue
# TODO: replace annotation with actual expected types once #1055 solved
kwargs = {'default': field.default} if not field.required else {}
merged_params[param_name] = Parameter(
param_name, Parameter.KEYWORD_ONLY, annotation=field.annotation, **kwargs
)
if config.extra is Extra.allow:
use_var_kw = True
if var_kw and use_var_kw:
# Make sure the parameter for extra kwargs
# does not have the same name as a field
default_model_signature = [
('__pydantic_self__', Parameter.POSITIONAL_OR_KEYWORD),
('data', Parameter.VAR_KEYWORD),
]
if [(p.name, p.kind) for p in present_params] == default_model_signature:
# if this is the standard model signature, use extra_data as the extra args name
var_kw_name = 'extra_data'
else:
# else start from var_kw
var_kw_name = var_kw.name
# generate a name that's definitely unique
while var_kw_name in fields:
var_kw_name += '_'
merged_params[var_kw_name] = var_kw.replace(name=var_kw_name)
return Signature(parameters=list(merged_params.values()), return_annotation=None)
def get_model(obj: Union[Type['BaseModel'], Type['Dataclass']]) -> Type['BaseModel']:
from .main import BaseModel
try:
model_cls = obj.__pydantic_model__ # type: ignore
except AttributeError:
model_cls = obj
if not issubclass(model_cls, BaseModel):
raise TypeError('Unsupported type, must be either BaseModel or dataclass')
return model_cls
def to_camel(string: str) -> str:
return ''.join(word.capitalize() for word in string.split('_'))
def to_lower_camel(string: str) -> str:
if len(string) >= 1:
pascal_string = to_camel(string)
return pascal_string[0].lower() + pascal_string[1:]
return string.lower()
T = TypeVar('T')
def unique_list(
input_list: Union[List[T], Tuple[T, ...]],
*,
name_factory: Callable[[T], str] = str,
) -> List[T]:
"""
Make a list unique while maintaining order.
We update the list if another one with the same name is set
(e.g. root validator overridden in subclass)
"""
result: List[T] = []
result_names: List[str] = []
for v in input_list:
v_name = name_factory(v)
if v_name not in result_names:
result_names.append(v_name)
result.append(v)
else:
result[result_names.index(v_name)] = v
return result
class PyObjectStr(str):
"""
String class where repr doesn't include quotes. Useful with Representation when you want to return a string
representation of something that valid (or pseudo-valid) python.
"""
def __repr__(self) -> str:
return str(self)
class Representation:
"""
Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.
__pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations
of objects.
"""
__slots__: Tuple[str, ...] = tuple()
def __repr_args__(self) -> 'ReprArgs':
"""
Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
Can either return:
* name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
* or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]`
"""
attrs = ((s, getattr(self, s)) for s in self.__slots__)
return [(a, v) for a, v in attrs if v is not None]
def __repr_name__(self) -> str:
"""
Name of the instance's class, used in __repr__.
"""
return self.__class__.__name__
def __repr_str__(self, join_str: str) -> str:
return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__())
def __pretty__(self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any, None, None]:
"""
Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects
"""
yield self.__repr_name__() + '('
yield 1
for name, value in self.__repr_args__():
if name is not None:
yield name + '='
yield fmt(value)
yield ','
yield 0
yield -1
yield ')'
def __str__(self) -> str:
return self.__repr_str__(' ')
def __repr__(self) -> str:
return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
def __rich_repr__(self) -> 'RichReprResult':
"""Get fields for Rich library"""
for name, field_repr in self.__repr_args__():
if name is None:
yield field_repr
else:
yield name, field_repr
class GetterDict(Representation):
"""
Hack to make object's smell just enough like dicts for validate_model.
We can't inherit from Mapping[str, Any] because it upsets cython so we have to implement all methods ourselves.
"""
__slots__ = ('_obj',)
def __init__(self, obj: Any):
self._obj = obj
def __getitem__(self, key: str) -> Any:
try:
return getattr(self._obj, key)
except AttributeError as e:
raise KeyError(key) from e
def get(self, key: Any, default: Any = None) -> Any:
return getattr(self._obj, key, default)
def extra_keys(self) -> Set[Any]:
"""
We don't want to get any other attributes of obj if the model didn't explicitly ask for them
"""
return set()
def keys(self) -> List[Any]:
"""
Keys of the pseudo dictionary, uses a list not set so order information can be maintained like python
dictionaries.
"""
return list(self)
def values(self) -> List[Any]:
return [self[k] for k in self]
def items(self) -> Iterator[Tuple[str, Any]]:
for k in self:
yield k, self.get(k)
def __iter__(self) -> Iterator[str]:
for name in dir(self._obj):
if not name.startswith('_'):
yield name
def __len__(self) -> int:
return sum(1 for _ in self)
def __contains__(self, item: Any) -> bool:
return item in self.keys()
def __eq__(self, other: Any) -> bool:
return dict(self) == dict(other.items())
def __repr_args__(self) -> 'ReprArgs':
return [(None, dict(self))]
def __repr_name__(self) -> str:
return f'GetterDict[{display_as_type(self._obj)}]'
class ValueItems(Representation):
"""
Class for more convenient calculation of excluded or included fields on values.
"""
__slots__ = ('_items', '_type')
def __init__(self, value: Any, items: Union['AbstractSetIntStr', 'MappingIntStrAny']) -> None:
items = self._coerce_items(items)
if isinstance(value, (list, tuple)):
items = self._normalize_indexes(items, len(value))
self._items: 'MappingIntStrAny' = items
def is_excluded(self, item: Any) -> bool:
"""
Check if item is fully excluded.
:param item: key or index of a value
"""
return self.is_true(self._items.get(item))
def is_included(self, item: Any) -> bool:
"""
Check if value is contained in self._items
:param item: key or index of value
"""
return item in self._items
def for_element(self, e: 'IntStr') -> Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']]:
"""
:param e: key or index of element on value
:return: raw values for element if self._items is dict and contain needed element
"""
item = self._items.get(e)
return item if not self.is_true(item) else None
def _normalize_indexes(self, items: 'MappingIntStrAny', v_length: int) -> 'DictIntStrAny':
"""
:param items: dict or set of indexes which will be normalized
:param v_length: length of sequence indexes of which will be
>>> self._normalize_indexes({0: True, -2: True, -1: True}, 4)
{0: True, 2: True, 3: True}
>>> self._normalize_indexes({'__all__': True}, 4)
{0: True, 1: True, 2: True, 3: True}
"""
normalized_items: 'DictIntStrAny' = {}
all_items = None
for i, v in items.items():
if not (isinstance(v, Mapping) or isinstance(v, AbstractSet) or self.is_true(v)):
raise TypeError(f'Unexpected type of exclude value for index "{i}" {v.__class__}')
if i == '__all__':
all_items = self._coerce_value(v)
continue
if not isinstance(i, int):
raise TypeError(
'Excluding fields from a sequence of sub-models or dicts must be performed index-wise: '
'expected integer keys or keyword "__all__"'
)
normalized_i = v_length + i if i < 0 else i
normalized_items[normalized_i] = self.merge(v, normalized_items.get(normalized_i))
if not all_items:
return normalized_items
if self.is_true(all_items):
for i in range(v_length):
normalized_items.setdefault(i, ...)
return normalized_items
for i in range(v_length):
normalized_item = normalized_items.setdefault(i, {})
if not self.is_true(normalized_item):
normalized_items[i] = self.merge(all_items, normalized_item)
return normalized_items
@classmethod
def merge(cls, base: Any, override: Any, intersect: bool = False) -> Any:
"""
Merge a ``base`` item with an ``override`` item.
Both ``base`` and ``override`` are converted to dictionaries if possible.
Sets are converted to dictionaries with the sets entries as keys and
Ellipsis as values.
Each key-value pair existing in ``base`` is merged with ``override``,
while the rest of the key-value pairs are updated recursively with this function.
Merging takes place based on the "union" of keys if ``intersect`` is
set to ``False`` (default) and on the intersection of keys if
``intersect`` is set to ``True``.
"""
override = cls._coerce_value(override)
base = cls._coerce_value(base)
if override is None:
return base
if cls.is_true(base) or base is None:
return override
if cls.is_true(override):
return base if intersect else override
# intersection or union of keys while preserving ordering:
if intersect:
merge_keys = [k for k in base if k in override] + [k for k in override if k in base]
else:
merge_keys = list(base) + [k for k in override if k not in base]
merged: 'DictIntStrAny' = {}
for k in merge_keys:
merged_item = cls.merge(base.get(k), override.get(k), intersect=intersect)
if merged_item is not None:
merged[k] = merged_item
return merged
@staticmethod
def _coerce_items(items: Union['AbstractSetIntStr', 'MappingIntStrAny']) -> 'MappingIntStrAny':
if isinstance(items, Mapping):
pass
elif isinstance(items, AbstractSet):
items = dict.fromkeys(items, ...)
else:
class_name = getattr(items, '__class__', '???')
assert_never(
items,
f'Unexpected type of exclude value {class_name}',
)
return items
@classmethod
def _coerce_value(cls, value: Any) -> Any:
if value is None or cls.is_true(value):
return value
return cls._coerce_items(value)
@staticmethod
def is_true(v: Any) -> bool:
return v is True or v is ...
def __repr_args__(self) -> 'ReprArgs':
return [(None, self._items)]
class ClassAttribute:
"""
Hide class attribute from its instances
"""
__slots__ = (
'name',
'value',
)
def __init__(self, name: str, value: Any) -> None:
self.name = name
self.value = value
def __get__(self, instance: Any, owner: Type[Any]) -> None:
if instance is None:
return self.value
raise AttributeError(f'{self.name!r} attribute of {owner.__name__!r} is class-only')
path_types = {
'is_dir': 'directory',
'is_file': 'file',
'is_mount': 'mount point',
'is_symlink': 'symlink',
'is_block_device': 'block device',
'is_char_device': 'char device',
'is_fifo': 'FIFO',
'is_socket': 'socket',
}
def path_type(p: 'Path') -> str:
"""
Find out what sort of thing a path is.
"""
assert p.exists(), 'path does not exist'
for method, name in path_types.items():
if getattr(p, method)():
return name
return 'unknown'
Obj = TypeVar('Obj')
def smart_deepcopy(obj: Obj) -> Obj:
"""
Return type as is for immutable built-in types
Use obj.copy() for built-in empty collections
Use copy.deepcopy() for non-empty collections and unknown objects
"""
obj_type = obj.__class__
if obj_type in IMMUTABLE_NON_COLLECTIONS_TYPES:
return obj # fastest case: obj is immutable and not collection therefore will not be copied anyway
try:
if not obj and obj_type in BUILTIN_COLLECTIONS:
# faster way for empty collections, no need to copy its members
return obj if obj_type is tuple else obj.copy() # type: ignore # tuple doesn't have copy method
except (TypeError, ValueError, RuntimeError):
# do we really dare to catch ALL errors? Seems a bit risky
pass
return deepcopy(obj) # slowest way when we actually might need a deepcopy
def is_valid_field(name: str) -> bool:
if not name.startswith('_'):
return True
return ROOT_KEY == name
DUNDER_ATTRIBUTES = {
'__annotations__',
'__classcell__',
'__doc__',
'__module__',
'__orig_bases__',
'__orig_class__',
'__qualname__',
}
def is_valid_private_name(name: str) -> bool:
return not is_valid_field(name) and name not in DUNDER_ATTRIBUTES
_EMPTY = object()
def all_identical(left: Iterable[Any], right: Iterable[Any]) -> bool:
"""
Check that the items of `left` are the same objects as those in `right`.
>>> a, b = object(), object()
>>> all_identical([a, b, a], [a, b, a])
True
>>> all_identical([a, b, [a]], [a, b, [a]]) # new list object, while "equal" is not "identical"
False
"""
for left_item, right_item in zip_longest(left, right, fillvalue=_EMPTY):
if left_item is not right_item:
return False
return True
def assert_never(obj: NoReturn, msg: str) -> NoReturn:
"""
Helper to make sure that we have covered all possible types.
This is mostly useful for ``mypy``, docs:
https://mypy.readthedocs.io/en/latest/literal_types.html#exhaustive-checks
"""
raise TypeError(msg)
def get_unique_discriminator_alias(all_aliases: Collection[str], discriminator_key: str) -> str:
"""Validate that all aliases are the same and if that's the case return the alias"""
unique_aliases = set(all_aliases)
if len(unique_aliases) > 1:
raise ConfigError(
f'Aliases for discriminator {discriminator_key!r} must be the same (got {", ".join(sorted(all_aliases))})'
)
return unique_aliases.pop()
def get_discriminator_alias_and_values(tp: Any, discriminator_key: str) -> Tuple[str, Tuple[str, ...]]:
"""
Get alias and all valid values in the `Literal` type of the discriminator field
`tp` can be a `BaseModel` class or directly an `Annotated` `Union` of many.
"""
is_root_model = getattr(tp, '__custom_root_type__', False)
if get_origin(tp) is Annotated:
tp = get_args(tp)[0]
if hasattr(tp, '__pydantic_model__'):
tp = tp.__pydantic_model__
if is_union(get_origin(tp)):
alias, all_values = _get_union_alias_and_all_values(tp, discriminator_key)
return alias, tuple(v for values in all_values for v in values)
elif is_root_model:
union_type = tp.__fields__[ROOT_KEY].type_
alias, all_values = _get_union_alias_and_all_values(union_type, discriminator_key)
if len(set(all_values)) > 1:
raise ConfigError(
f'Field {discriminator_key!r} is not the same for all submodels of {display_as_type(tp)!r}'
)
return alias, all_values[0]
else:
try:
t_discriminator_type = tp.__fields__[discriminator_key].type_
except AttributeError as e:
raise TypeError(f'Type {tp.__name__!r} is not a valid `BaseModel` or `dataclass`') from e
except KeyError as e:
raise ConfigError(f'Model {tp.__name__!r} needs a discriminator field for key {discriminator_key!r}') from e
if not is_literal_type(t_discriminator_type):
raise ConfigError(f'Field {discriminator_key!r} of model {tp.__name__!r} needs to be a `Literal`')
return tp.__fields__[discriminator_key].alias, all_literal_values(t_discriminator_type)
def _get_union_alias_and_all_values(
union_type: Type[Any], discriminator_key: str
) -> Tuple[str, Tuple[Tuple[str, ...], ...]]:
zipped_aliases_values = [get_discriminator_alias_and_values(t, discriminator_key) for t in get_args(union_type)]
# unzip: [('alias_a',('v1', 'v2)), ('alias_b', ('v3',))] => [('alias_a', 'alias_b'), (('v1', 'v2'), ('v3',))]
all_aliases, all_values = zip(*zipped_aliases_values)
return get_unique_discriminator_alias(all_aliases, discriminator_key), all_values