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df1f7bcd0e
This pull request allows to return predictions in raw cell representation from table transformer. It will be later used to save prediction in a cells format for simpler metrics calculation. This PR has to be merged, after https://github.com/Unstructured-IO/unstructured-inference/pull/335
34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
import pytest
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from unstructured.metrics.table.table_formats import SimpleTableCell
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@pytest.mark.parametrize(
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("row_nums", "column_nums", "x", "y", "w", "h"),
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[
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([3, 2, 1], [6, 7], 6, 1, 2, 3),
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([2], [6, 7], 6, 2, 2, 1),
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([1, 2, 3], [20], 20, 1, 1, 3),
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([5], [5], 5, 5, 1, 1),
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],
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)
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def test_simple_table_cell_parsing_from_table_transformer_when_expected_input(
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row_nums, column_nums, x, y, w, h
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):
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table_transformer_cell = {"row_nums": row_nums, "column_nums": column_nums, "cell text": "text"}
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transformed_cell = SimpleTableCell.from_table_transformer_cell(table_transformer_cell)
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expected_cell = SimpleTableCell(x=x, y=y, w=w, h=h, content="text")
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assert expected_cell == transformed_cell
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def test_simple_table_cell_parsing_from_table_transformer_when_missing_row_nums():
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cell = {"row_nums": [], "column_nums": [1], "cell text": "text"}
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with pytest.raises(ValueError, match='has missing values under "row_nums" key'):
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SimpleTableCell.from_table_transformer_cell(cell)
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def test_simple_table_cell_parsing_from_table_transformer_when_missing_column_nums():
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cell = {"row_nums": [1], "column_nums": [], "cell text": "text"}
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with pytest.raises(ValueError, match='has missing values under "column_nums" key'):
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SimpleTableCell.from_table_transformer_cell(cell)
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