143 lines
3.3 KiB
Python
143 lines
3.3 KiB
Python
from itertools import starmap, repeat
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import numpy as np
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import pytest
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from PIL import Image
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from funcy import lmap, compose, flatten, lflatten, omit
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from pyinfra.server.dispatcher.dispatcher import Nothing
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from pyinfra.server.normalization import normalize_item
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from pyinfra.server.packing import pack, unpack
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from pyinfra.utils.func import star, lift, lstarlift
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from test.utils.image import image_to_bytes
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from test.utils.pdf import pdf_stream
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@pytest.fixture
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def data(data_type, pdf):
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if data_type == "pdf":
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return pdf
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elif data_type == "bytestring":
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return "content".encode("latin1")
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@pytest.fixture
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def input_data_items(unencoded_input_data, input_data_encoder):
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return input_data_encoder(unencoded_input_data)
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@pytest.fixture
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def unencoded_input_data(item_type, unencoded_strings, unencoded_images, unencoded_pdfs):
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if item_type == "string":
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return unencoded_strings
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elif item_type == "image":
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return unencoded_images
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elif item_type == "pdf":
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return unencoded_pdfs
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else:
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raise ValueError(f"Unknown item type {item_type}")
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@pytest.fixture
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def input_data_encoder(item_type):
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if item_type == "string":
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return strings_to_bytes
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elif item_type == "image":
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return images_to_bytes
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elif item_type == "pdf":
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return pdfs_to_bytes
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else:
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raise ValueError(f"Unknown item type {item_type}")
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@pytest.fixture
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def unencoded_pdfs(n_items, unencoded_pdf):
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return [unencoded_pdf] * n_items
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def pdfs_to_bytes(unencoded_pdfs):
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return [pdf_stream(pdf) for pdf in unencoded_pdfs]
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@pytest.fixture
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def target_data_items(input_data_items, core_operation, metadata):
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if core_operation is Nothing:
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return Nothing
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op = compose(normalize_item, core_operation)
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expected = lflatten(starmap(op, zip(input_data_items, metadata)))
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return expected
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@pytest.fixture
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def unencoded_strings(n_items):
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return [f"content{i}" for i in range(n_items)]
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def strings_to_bytes(strings):
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return [bytes(s, encoding="utf8") for s in strings]
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@pytest.fixture
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def targets(input_data_items, operation, metadata):
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if operation is Nothing:
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return Nothing
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op = compose(lift(star(pack)), normalize_item, operation)
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expected = lmap(unpack, flatten(starmap(op, zip(input_data_items, metadata))))
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return expected
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@pytest.fixture
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def endpoint(url):
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return f"{url}/submit"
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@pytest.fixture(params=["rest", "basic"])
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def client_pipeline_type(request):
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return request.param
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@pytest.fixture(params=[1, 0, 5])
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def n_items(request):
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return request.param
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@pytest.fixture(params=[0, 100])
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def n_pages(request):
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return request.param
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@pytest.fixture(params=[1, 5])
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def buffer_size(request):
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return request.param
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def array_to_image(array) -> Image.Image:
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return Image.fromarray(np.uint8(array * 255), mode="RGB")
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def input_batch(n_items):
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return np.random.random_sample(size=(n_items, 3, 30, 30))
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@pytest.fixture
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def unencoded_images(n_items):
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return lmap(array_to_image, input_batch(n_items))
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def images_to_bytes(images):
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return lmap(image_to_bytes, images)
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@pytest.fixture
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def metadata(n_items):
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return list(repeat({"key": "value", "pages": [0, 2, 3]}, n_items))
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@pytest.fixture
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def packages(input_data_items, metadata):
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return lstarlift(pack)(zip(input_data_items, metadata))
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