pyinfra/test/fixtures/input.py

70 lines
1.7 KiB
Python

from itertools import starmap, repeat
import numpy as np
import pytest
from PIL import Image
from funcy import lmap, compose, flatten
from pyinfra.server.rest import pack, normalize_item, unpack
from pyinfra.utils.func import star, lift
from test.utils.image import image_to_bytes
@pytest.fixture
def data(data_type, pdf):
if data_type == "pdf":
return pdf
elif data_type == "bytestring":
return "content".encode("latin1")
@pytest.fixture
def input_data_items(item_type, n_items, pdf):
if item_type == "string":
return [bytes(f"content{i}", encoding="utf8") for i in range(n_items)]
elif item_type == "image":
return images(n_items)
elif item_type == "pdf":
return [pdf] * n_items
else:
raise ValueError(f"Unknown item type {item_type}")
@pytest.fixture
def target_data_items(input_data_items, item_type, operation, metadata):
op = compose(lift(star(pack)), normalize_item, operation)
expected = lmap(unpack, flatten(starmap(op, zip(input_data_items, metadata))))
return expected
@pytest.fixture(params=[0, 1, 5, 10])
def n_items(request):
return request.param
@pytest.fixture(params=[0, 5, 100])
def n_pages(request):
return request.param
def array_to_image(array) -> Image.Image:
return Image.fromarray(np.uint8(array * 255), mode="RGB")
def input_batch(n_items):
return np.random.random_sample(size=(n_items, 3, 30, 30))
@pytest.fixture(params=[1, 5, 90])
def buffer_size(request):
return request.param
def images(n_items):
return lmap(compose(image_to_bytes, array_to_image), input_batch(n_items))
@pytest.fixture
def metadata(n_items):
return list(repeat({"dummy": True}, n_items))