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?? ..
?? __init__.py(2.88 KB)
?? __pycache__
?? _hypothesis_plugin.py(14.5 KB)
?? annotated_types.py(3.08 KB)
?? class_validators.py(14.33 KB)
?? color.py(16.45 KB)
?? config.py(6.38 KB)
?? dataclasses.py(17.75 KB)
?? datetime_parse.py(7.54 KB)
?? decorator.py(10.1 KB)
?? env_settings.py(13.77 KB)
?? error_wrappers.py(5.07 KB)
?? errors.py(17.31 KB)
?? fields.py(49.46 KB)
?? generics.py(17.45 KB)
?? json.py(3.31 KB)
?? main.py(43.5 KB)
?? mypy.py(37.87 KB)
?? networks.py(21.61 KB)
?? parse.py(1.78 KB)
?? py.typed(0 B)
?? schema.py(46.64 KB)
?? tools.py(2.81 KB)
?? types.py(34.62 KB)
?? typing.py(18.93 KB)
?? utils.py(25.31 KB)
?? validators.py(21.48 KB)
?? version.py(1.01 KB)

Editing: tools.py

import json
from functools import lru_cache
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Optional, Type, TypeVar, Union

from pydantic.v1.parse import Protocol, load_file, load_str_bytes
from pydantic.v1.types import StrBytes
from pydantic.v1.typing import display_as_type

__all__ = ('parse_file_as', 'parse_obj_as', 'parse_raw_as', 'schema_of', 'schema_json_of')

NameFactory = Union[str, Callable[[Type[Any]], str]]

if TYPE_CHECKING:
    from pydantic.v1.typing import DictStrAny

def _generate_parsing_type_name(type_: Any) -> str:
    return f'ParsingModel[{display_as_type(type_)}]'

@lru_cache(maxsize=2048)
def _get_parsing_type(type_: Any, *, type_name: Optional[NameFactory] = None) -> Any:
    from pydantic.v1.main import create_model

if type_name is None:
        type_name = _generate_parsing_type_name
    if not isinstance(type_name, str):
        type_name = type_name(type_)
    return create_model(type_name, __root__=(type_, ...))

T = TypeVar('T')

def parse_obj_as(type_: Type[T], obj: Any, *, type_name: Optional[NameFactory] = None) -> T:
    model_type = _get_parsing_type(type_, type_name=type_name)  # type: ignore[arg-type]
    return model_type(__root__=obj).__root__

def parse_file_as(
    type_: Type[T],
    path: Union[str, Path],
    *,
    content_type: str = None,
    encoding: str = 'utf8',
    proto: Protocol = None,
    allow_pickle: bool = False,
    json_loads: Callable[[str], Any] = json.loads,
    type_name: Optional[NameFactory] = None,
) -> T:
    obj = load_file(
        path,
        proto=proto,
        content_type=content_type,
        encoding=encoding,
        allow_pickle=allow_pickle,
        json_loads=json_loads,
    )
    return parse_obj_as(type_, obj, type_name=type_name)

def parse_raw_as(
    type_: Type[T],
    b: StrBytes,
    *,
    content_type: str = None,
    encoding: str = 'utf8',
    proto: Protocol = None,
    allow_pickle: bool = False,
    json_loads: Callable[[str], Any] = json.loads,
    type_name: Optional[NameFactory] = None,
) -> T:
    obj = load_str_bytes(
        b,
        proto=proto,
        content_type=content_type,
        encoding=encoding,
        allow_pickle=allow_pickle,
        json_loads=json_loads,
    )
    return parse_obj_as(type_, obj, type_name=type_name)

def schema_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_kwargs: Any) -> 'DictStrAny':
    """Generate a JSON schema (as dict) for the passed model or dynamically generated one"""
    return _get_parsing_type(type_, type_name=title).schema(**schema_kwargs)

def schema_json_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_json_kwargs: Any) -> str:
    """Generate a JSON schema (as JSON) for the passed model or dynamically generated one"""
    return _get_parsing_type(type_, type_name=title).schema_json(**schema_json_kwargs)

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