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96 lines
2.5 KiB
Python
96 lines
2.5 KiB
Python
import random
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import pytest
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import torch
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from refiners.training_utils.common import TimeUnit, TimeValue, TimeValueInput, parse_number_unit_field, scoped_seed
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@pytest.mark.parametrize(
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"input_value, expected_output",
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[
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("10: step", TimeValue(number=10, unit=TimeUnit.STEP)),
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("20 :epoch", TimeValue(number=20, unit=TimeUnit.EPOCH)),
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("30: Iteration", TimeValue(number=30, unit=TimeUnit.ITERATION)),
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(50, TimeValue(number=50, unit=TimeUnit.DEFAULT)),
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({"number": 100, "unit": "STEP"}, TimeValue(number=100, unit=TimeUnit.STEP)),
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(TimeValue(number=200, unit=TimeUnit.EPOCH), TimeValue(number=200, unit=TimeUnit.EPOCH)),
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],
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)
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def test_parse_number_unit_field(input_value: TimeValueInput, expected_output: TimeValue):
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result = parse_number_unit_field(input_value)
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assert result == expected_output
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@pytest.mark.parametrize(
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"invalid_input",
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[
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"invalid:input",
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{"number": "not_a_number", "unit": "step"},
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{"invalid_key": 10},
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None,
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],
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)
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def test_parse_number_unit_field_invalid_input(invalid_input: TimeValueInput):
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with pytest.raises(ValueError):
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parse_number_unit_field(invalid_input)
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@scoped_seed(seed=37)
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def pick_a_number() -> int:
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return int(torch.randint(0, 100, (1,)).item())
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@pytest.mark.parametrize(
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"seed, expected_output",
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[
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(42, 42),
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(37, 31),
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(0, 44),
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],
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)
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def test_scoped_seed_with_specific_seed(seed: int, expected_output: int) -> None:
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with scoped_seed(seed):
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assert torch.randint(0, 100, (1,)).item() == expected_output
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@pytest.mark.parametrize(
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"seed, expected_output",
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[
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(42, 81),
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(37, 87),
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(0, 49),
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],
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)
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def test_scoped_seed_with_random_module(seed: int, expected_output: int) -> None:
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with scoped_seed(seed):
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assert random.randint(0, 100) == expected_output
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def test_scoped_seed_with_function_call() -> None:
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assert pick_a_number() == 31
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with scoped_seed(37):
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assert pick_a_number() == 31
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def test_scoped_seed_with_callable_seed() -> None:
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with scoped_seed(pick_a_number):
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assert pick_a_number() == 31
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def add_10(n: int) -> int:
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return n + 10
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@scoped_seed(seed=add_10)
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def pick_a_number_greater_than_n_plus_10(n: int) -> int:
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return int(torch.randint(n, 100, (1,)).item())
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assert pick_a_number_greater_than_n_plus_10(10) == 81
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def test_scoped_seed_restore_state() -> None:
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random.seed(37)
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with scoped_seed(42):
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random.randint(0, 100)
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assert random.randint(0, 100) == 87
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