tx_flow_analysis/venv/lib/python3.10/site-packages/pydantic/v1/json.py

import datetime
from collections import deque
from decimal import Decimal
from enum import Enum
from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
from pathlib import Path
from re import Pattern
from types import GeneratorType
from typing import Any, Callable, Dict, Type, Union
from uuid import UUID

from pydantic.v1.color import Color
from pydantic.v1.networks import NameEmail
from pydantic.v1.types import SecretBytes, SecretStr

__all__ = 'pydantic_encoder', 'custom_pydantic_encoder', 'timedelta_isoformat'


def isoformat(o: Union[datetime.date, datetime.time]) -> str:
    return o.isoformat()


def decimal_encoder(dec_value: Decimal) -> Union[int, float]:
    """
    Encodes a Decimal as int of there's no exponent, otherwise float

    This is useful when we use ConstrainedDecimal to represent Numeric(x,0)
    where a integer (but not int typed) is used. Encoding this as a float
    results in failed round-tripping between encode and parse.
    Our Id type is a prime example of this.

    >>> decimal_encoder(Decimal("1.0"))
    1.0

    >>> decimal_encoder(Decimal("1"))
    1
    """
    if dec_value.as_tuple().exponent >= 0:
        return int(dec_value)
    else:
        return float(dec_value)


ENCODERS_BY_TYPE: Dict[Type[Any], Callable[[Any], Any]] = {
    bytes: lambda o: o.decode(),
    Color: str,
    datetime.date: isoformat,
    datetime.datetime: isoformat,
    datetime.time: isoformat,
    datetime.timedelta: lambda td: td.total_seconds(),
    Decimal: decimal_encoder,
    Enum: lambda o: o.value,
    frozenset: list,
    deque: list,
    GeneratorType: list,
    IPv4Address: str,
    IPv4Interface: str,
    IPv4Network: str,
    IPv6Address: str,
    IPv6Interface: str,
    IPv6Network: str,
    NameEmail: str,
    Path: str,
    Pattern: lambda o: o.pattern,
    SecretBytes: str,
    SecretStr: str,
    set: list,
    UUID: str,
}


def pydantic_encoder(obj: Any) -> Any:
    from dataclasses import asdict, is_dataclass

    from pydantic.v1.main import BaseModel

    if isinstance(obj, BaseModel):
        return obj.dict()
    elif is_dataclass(obj):
        return asdict(obj)

    # Check the class type and its superclasses for a matching encoder
    for base in obj.__class__.__mro__[:-1]:
        try:
            encoder = ENCODERS_BY_TYPE[base]
        except KeyError:
            continue
        return encoder(obj)
    else:  # We have exited the for loop without finding a suitable encoder
        raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable")


def custom_pydantic_encoder(type_encoders: Dict[Any, Callable[[Type[Any]], Any]], obj: Any) -> Any:
    # Check the class type and its superclasses for a matching encoder
    for base in obj.__class__.__mro__[:-1]:
        try:
            encoder = type_encoders[base]
        except KeyError:
            continue

        return encoder(obj)
    else:  # We have exited the for loop without finding a suitable encoder
        return pydantic_encoder(obj)


def timedelta_isoformat(td: datetime.timedelta) -> str:
    """
    ISO 8601 encoding for Python timedelta object.
    """
    minutes, seconds = divmod(td.seconds, 60)
    hours, minutes = divmod(minutes, 60)
    return f'{"-" if td.days < 0 else ""}P{abs(td.days)}DT{hours:d}H{minutes:d}M{seconds:d}.{td.microseconds:06d}S'