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This is, for the majority of backends, the native format anyway, and so we save encoding and decoding when using the PickleCodec or if (in future) a orjson Coder was to be added. For the JsonCodec, the only thing that changed is the location where the JSON data is encoded to bytes and decoded back again to a string.
67 lines
1.8 KiB
Python
67 lines
1.8 KiB
Python
from dataclasses import dataclass
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from typing import Any, Optional
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import pytest
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from pydantic import BaseModel, ValidationError
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from fastapi_cache.coder import JsonCoder, PickleCoder
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@dataclass
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class DCItem:
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name: str
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price: float
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description: Optional[str] = None
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tax: Optional[float] = None
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class PDItem(BaseModel):
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name: str
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price: float
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description: Optional[str] = None
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tax: Optional[float] = None
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@pytest.mark.parametrize(
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"value",
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[
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1,
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"some_string",
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(1, 2),
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[1, 2, 3],
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{"some_key": 1, "other_key": 2},
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DCItem(name="foo", price=42.0, description="some dataclass item", tax=0.2),
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PDItem(name="foo", price=42.0, description="some pydantic item", tax=0.2),
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],
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)
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def test_pickle_coder(value: Any) -> None:
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encoded_value = PickleCoder.encode(value)
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assert isinstance(encoded_value, bytes)
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decoded_value = PickleCoder.decode(encoded_value)
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assert decoded_value == value
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@pytest.mark.parametrize(
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("value", "return_type"),
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[
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(1, None),
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("some_string", None),
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((1, 2), tuple[int, int]),
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([1, 2, 3], None),
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({"some_key": 1, "other_key": 2}, None),
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(DCItem(name="foo", price=42.0, description="some dataclass item", tax=0.2), DCItem),
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(PDItem(name="foo", price=42.0, description="some pydantic item", tax=0.2), PDItem),
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],
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)
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def test_json_coder(value: Any, return_type) -> None:
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encoded_value = JsonCoder.encode(value)
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assert isinstance(encoded_value, bytes)
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decoded_value = JsonCoder.decode_as_type(encoded_value, type_=return_type)
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assert decoded_value == value
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def test_json_coder_validation_error() -> None:
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invalid = b'{"name": "incomplete"}'
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with pytest.raises(ValidationError):
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JsonCoder.decode_as_type(invalid, type_=PDItem)
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